{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T12:40:30Z","timestamp":1779194430005,"version":"3.51.4"},"reference-count":203,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2020,12,8]],"date-time":"2020-12-08T00:00:00Z","timestamp":1607385600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Collecting and analyzing massive data generated from smart devices have become increasingly pervasive in crowdsensing, which are the building blocks for data-driven decision-making. However, extensive statistics and analysis of such data will seriously threaten the privacy of participating users. Local differential privacy (LDP) was proposed as an excellent and prevalent privacy model with distributed architecture, which can provide strong privacy guarantees for each user while collecting and analyzing data. LDP ensures that each user\u2019s data is locally perturbed first in the client-side and then sent to the server-side, thereby protecting data from privacy leaks on both the client-side and server-side. This survey presents a comprehensive and systematic overview of LDP with respect to privacy models, research tasks, enabling mechanisms, and various applications. Specifically, we first provide a theoretical summarization of LDP, including the LDP model, the variants of LDP, and the basic framework of LDP algorithms. Then, we investigate and compare the diverse LDP mechanisms for various data statistics and analysis tasks from the perspectives of frequency estimation, mean estimation, and machine learning. Furthermore, we also summarize practical LDP-based application scenarios. Finally, we outline several future research directions under LDP.<\/jats:p>","DOI":"10.3390\/s20247030","type":"journal-article","created":{"date-parts":[[2020,12,8]],"date-time":"2020-12-08T09:17:04Z","timestamp":1607419024000},"page":"7030","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":87,"title":["A Comprehensive Survey on Local Differential Privacy toward Data Statistics and Analysis"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3067-4674","authenticated-orcid":false,"given":"Teng","family":"Wang","sequence":"first","affiliation":[{"name":"School of Cyberspace Security, Xi\u2019an University of Posts and Telecommunications, Xi\u2019an 710121, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6056-667X","authenticated-orcid":false,"given":"Xuefeng","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Cyberspace Security, Xi\u2019an University of Posts and Telecommunications, Xi\u2019an 710121, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jingyu","family":"Feng","sequence":"additional","affiliation":[{"name":"School of Cyberspace Security, Xi\u2019an University of Posts and Telecommunications, Xi\u2019an 710121, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinyu","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,12,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1489","DOI":"10.1109\/JIOT.2017.2714189","article-title":"Mobile Big Data: The Fuel for Data-Driven Wireless","volume":"4","author":"Cheng","year":"2017","journal-title":"IEEE Internet Things J."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2818183","article-title":"Mobile Crowd Sensing and Computing: The Review of an Emerging Human-Powered Sensing Paradigm","volume":"48","author":"Guo","year":"2015","journal-title":"ACM Comput. Surv."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Shu, J., Jia, X., Yang, K., and Wang, H. (2018). Privacy-Preserving Task Recommendation Services for Crowdsourcing. IEEE Trans. Services Comput., 1\u201313.","DOI":"10.1109\/TSC.2018.2791601"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1630","DOI":"10.1109\/TKDE.2018.2866863","article-title":"A Study on Big Knowledge and Its Engineering Issues","volume":"31","author":"Lu","year":"2019","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"202","DOI":"10.1109\/TSC.2017.2674662","article-title":"Crowdsourcing, Mixed Elastic Systems and Human-Enhanced Computing\u2014 A Survey","volume":"11","author":"Jarrett","year":"2018","journal-title":"IEEE Trans. Serv. Comput."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.ijhcs.2014.12.003","article-title":"Collaborative filtering for people-to-people recommendation in online dating: Data analysis and user trial","volume":"76","author":"Krzywicki","year":"2015","journal-title":"Int. J. Hum.-Comput. Stud."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Chen, R., Li, H., Qin, A.K., Kasiviswanathan, S.P., and Jin, H. (2016, January 16\u201320). Private spatial data aggregation in the local setting. Proceedings of the 2016 IEEE 32nd International Conference on Data Engineering (ICDE), Helsinki, Finland.","DOI":"10.1109\/ICDE.2016.7498248"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1109\/TSG.2014.2343997","article-title":"Efficient histogram estimation for smart grid data processing with the loglog-Bloom-filter","volume":"6","author":"Yao","year":"2014","journal-title":"IEEE Trans. Smart Grid"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1767","DOI":"10.1109\/TII.2018.2809672","article-title":"A practical privacy-preserving data aggregation (3PDA) scheme for smart grid","volume":"15","author":"Liu","year":"2018","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/1749603.1749605","article-title":"Privacy-preserving data publishing: A survey of recent developments","volume":"42","author":"Fung","year":"2010","journal-title":"ACM Comput. Surv."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1619","DOI":"10.1109\/TKDE.2017.2697856","article-title":"Differentially Private Data Publishing and Analysis: A Survey","volume":"29","author":"Zhu","year":"2017","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1250","DOI":"10.1109\/JIOT.2017.2694844","article-title":"A survey on security and privacy issues in Internet-of-Things","volume":"4","author":"Yang","year":"2017","journal-title":"IEEE Internet Things J."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1007\/s41019-015-0001-x","article-title":"Big data privacy: Challenges to privacy principles and models","volume":"1","year":"2016","journal-title":"Data Sci. Eng."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2751","DOI":"10.1109\/ACCESS.2016.2577036","article-title":"Big privacy: Challenges and opportunities of privacy study in the age of big data","volume":"4","author":"Yu","year":"2016","journal-title":"IEEE Access"},{"key":"ref_15","first-page":"146","article-title":"Privacy and security in the big data paradigm","volume":"60","author":"Sun","year":"2020","journal-title":"J. Comput. Inf. Syst."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1048","DOI":"10.1109\/TSG.2013.2240319","article-title":"A versatile clustering method for electricity consumption pattern analysis in households","volume":"4","author":"Hino","year":"2013","journal-title":"IEEE Trans. Smart Grid"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Zhao, J., Jung, T., Wang, Y., and Li, X. (May, January 27). Achieving differential privacy of data disclosure in the smart grid. Proceedings of the IEEE INFOCOM 2014\u2014IEEE Conference on Computer Communications, Toronto, ON, Canada.","DOI":"10.1109\/INFOCOM.2014.6847974"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1016\/j.ins.2016.08.011","article-title":"A technique to provide differential privacy for appliance usage in smart metering","volume":"370","author":"Barbosa","year":"2016","journal-title":"Inf. Sci."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Wang, T., Zhao, J., Yu, H., Liu, J., Yang, X., Ren, X., and Shi, S. (2019, January 3\u20137). Privacy-preserving Crowd-guided AI Decision-making in Ethical Dilemmas. Proceedings of the 28th ACM International Conference on Information and Knowledge Management, Beijing, China.","DOI":"10.1145\/3357384.3357954"},{"key":"ref_20","unstructured":"(2018, May 25). General Data Protection Regulation GDPR. Available online: https:\/\/gdpr-info.eu\/."},{"key":"ref_21","unstructured":"(2019, July 11). Privacy Framework, Available online: https:\/\/www.nist.gov\/privacy-framework."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1561\/0400000042","article-title":"The algorithmic foundations of differential privacy","volume":"9","author":"Dwork","year":"2014","journal-title":"Found. Trends\u00ae Theor. Comput. Sci."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Yang, X., Wang, T., Ren, X., and Yu, W. (2017). Survey on improving data utility in differentially private sequential data publishing. IEEE Trans. Big Data, 1\u201319.","DOI":"10.1109\/TBDATA.2017.2715334"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Abowd, J.M. (2018, January 18\u201323). The U.S. Census Bureau Adopts Differential Privacy. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, London, UK.","DOI":"10.1145\/3219819.3226070"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"793","DOI":"10.1137\/090756090","article-title":"What can we learn privately?","volume":"40","author":"Kasiviswanathan","year":"2011","journal-title":"SIAM J. Comput."},{"key":"ref_26","unstructured":"(2017, December 31). Learning with Privacy at Scale. Available online: https:\/\/machinelearning.apple.com\/research\/learning-with-privacy-at-scale."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Erlingsson, \u00da., Pihur, V., and Korolova, A. (2014, January 3\u20137). Rappor: Randomized aggregatable privacy-preserving ordinal response. Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security, Scottsdale, AZ, USA.","DOI":"10.1145\/2660267.2660348"},{"key":"ref_28","unstructured":"Ding, B., Kulkarni, J., and Yekhanin, S. (2017). Collecting telemetry data privately. Advances in Neural Information Processing Systems, Curran Associates, Inc."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Wang, N., Xiao, X., Yang, Y., Zhao, J., Hui, S.C., Shin, H., Shin, J., and Yu, G. (2019, January 8\u201311). Collecting and Analyzing Multidimensional Data with Local Differential Privacy. Proceedings of the 2019 IEEE 35th International Conference on Data Engineering (ICDE), Macao, China.","DOI":"10.1109\/ICDE.2019.00063"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Qin, Z., Yang, Y., Yu, T., Khalil, I., Xiao, X., and Ren, K. (2016, January 24\u201328). Heavy hitter estimation over set-valued data with local differential privacy. Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, Vienna, Austria.","DOI":"10.1145\/2976749.2978409"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Cormode, G., Kulkarni, T., and Srivastava, D. (2018, January 10\u201315). Marginal release under local differential privacy. Proceedings of the 2018 International Conference on Management of Data, Houston, TX, USA.","DOI":"10.1145\/3183713.3196906"},{"key":"ref_32","unstructured":"Wang, D., Gaboardi, M., and Xu, J. (2018). Empirical risk minimization in non-interactive local differential privacy revisited. Advances in Neural Information Processing Systems, Curran Associates, Inc."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Wang, Y., Tong, Y., and Shi, D. (2020, January 7\u201312). Federated Latent Dirichlet Allocation: A Local Differential Privacy Based Framework. Proceedings of the AAAI Conference on Artificial Intelligence, New York, NY, USA.","DOI":"10.1609\/aaai.v34i04.6096"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"5827","DOI":"10.1109\/JIOT.2019.2952146","article-title":"Local Differential Privacy for Deep Learning","volume":"7","author":"Arachchige","year":"2019","journal-title":"IEEE Internet Things J."},{"key":"ref_35","unstructured":"Wang, T., Blocki, J., Li, N., and Jha, S. (2017, January 15\u201317). Locally differentially private protocols for frequency estimation. Proceedings of the 26th USENIX Security Symposium (USENIX Security 17), Baltimore, MD, USA."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Ye, Q., and Hu, H. (2019, January 26\u201330). Local Differential Privacy: Tools, Challenges, and Opportunities. Proceedings of the Workshop of Web Information Systems Engineering (WISE), Hong Kong, SAR, China.","DOI":"10.1007\/978-981-15-3281-8_2"},{"key":"ref_37","unstructured":"Bebensee, B. (2019). Local differential privacy: A tutorial. arXiv."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Li, N., and Ye, Q. (2019, January 10\u201313). Mobile Data Collection and Analysis with Local Differential Privacy. Proceedings of the IEEE International Conference on Mobile Data Management (MDM), Hong Kong, SAR, China.","DOI":"10.1109\/MDM.2019.00-80"},{"key":"ref_39","first-page":"1","article-title":"A survey of local differential privacy for securing internet of vehicles","volume":"76","author":"Zhao","year":"2019","journal-title":"J. Supercomput."},{"key":"ref_40","unstructured":"Yang, M., Lyu, L., Zhao, J., Zhu, T., and Lam, K.Y. (2020). Local Differential Privacy and Its Applications: A Comprehensive Survey. arXiv."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"8829523","DOI":"10.1155\/2020\/8829523","article-title":"A Comprehensive Survey on Local Differential Privacy","volume":"2020","author":"Xiong","year":"2020","journal-title":"Secur. Commun. Netw."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Duchi, J.C., Jordan, M.I., and Wainwright, M.J. (2013, January 26\u201329). Local privacy and statistical minimax rates. Proceedings of the IEEE Annual Symposium on Foundations of Computer Science, Berkeley, CA, USA.","DOI":"10.1109\/FOCS.2013.53"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1080\/01621459.1965.10480775","article-title":"Randomized response: A survey technique for eliminating evasive answer bias","volume":"60","author":"Warner","year":"1965","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_44","unstructured":"Wang, Y., Wu, X., and Hu, D. (2016, January 15\u201316). Using Randomized Response for Differential Privacy Preserving Data Collection. Proceedings of the EDBT\/ICDT Workshops, Bordeaux, France."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Dwork, C., McSherry, F., Nissim, K., and Smith, A. (2006, January 4\u20137). Calibrating Noise to Sensitivity in Private Data Analysis. Proceedings of the Theory of Cryptography Conference, New York, NY, USA.","DOI":"10.1007\/11681878_14"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"McSherry, F., and Talwar, K. (2007, January 20\u201323). Mechanism Design via Differential Privacy. Proceedings of the IEEE Symposium on Foundations of Computer Science (FOCS), Providence, RI, USA.","DOI":"10.1109\/FOCS.2007.66"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Duchi, J.C., Jordan, M.I., and Wainwright, M.J. (2013). Local privacy, data processing inequalities, and statistical minimax rates. arXiv.","DOI":"10.1109\/FOCS.2013.53"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Joseph, M., Mao, J., Neel, S., and Roth, A. (2019, January 9\u201312). The Role of Interactivity in Local Differential Privacy. Proceedings of the IEEE Annual Symposium on Foundations of Computer Science (FOCS), Baltimore, MD, USA.","DOI":"10.1109\/FOCS.2019.00015"},{"key":"ref_49","unstructured":"Wang, D., and Xu, J. (2019, January 9\u201315). On sparse linear regression in the local differential privacy model. Proceedings of the International Conference on Machine Learning, Long Beach, CA, USA."},{"key":"ref_50","unstructured":"Dwork, C., Kenthapadi, K., McSherry, F., Mironov, I., and Naor, M. (June, January 28). Our data, ourselves: Privacy via distributed noise generation. Proceedings of the Theory and Applications of Cryptographic Techniques, St. Petersburg, Russia."},{"key":"ref_51","unstructured":"Bassily, R. (2019, January 16\u201318). Linear queries estimation with local differential privacy. Proceedings of the International Conference on Artificial Intelligence and Statistics, Naha, Okinawa, Japan."},{"key":"ref_52","unstructured":"Avent, B., Korolova, A., Zeber, D., Hovden, T., and Livshits, B. (2017, January 16\u201318). BLENDER: Enabling local search with a hybrid differential privacy model. Proceedings of the USENIX Security Symposium, Vancouver, BC, Canada."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Andr\u00e9s, M., Bordenabe, N., Chatzikokolakis, K., and Palamidessi, C. (2013, January 4\u20138). Geo-Indistinguishability: Differential Privacy for Location-Based Systems. Proceedings of the 2013 ACM SIGSAC Conference on Computer & Communications Security, Berlin, Germany.","DOI":"10.1145\/2508859.2516735"},{"key":"ref_54","unstructured":"Alvim, M.S., Chatzikokolakis, K., Palamidessi, C., and Pazii, A. (2018). Metric-based local differential privacy for statistical applications. arXiv."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Chatzikokolakis, K., Andr\u00e9s, M.E., Bordenabe, N.E., and Palamidessi, C. (2013, January 10\u201312). Broadening the scope of differential privacy using metrics. Proceedings of the International Symposium on Privacy Enhancing Technologies Symposium, Bloomington, IN, USA.","DOI":"10.1007\/978-3-642-39077-7_5"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Gursoy, M.E., Tamersoy, A., Truex, S., Wei, W., and Liu, L. (2019). Secure and Utility-Aware Data Collection with Condensed Local Differential Privacy. IEEE Trans. Dependable Secur. Comput., 1\u201313.","DOI":"10.1109\/TDSC.2019.2949041"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"655","DOI":"10.1109\/TKDE.2018.2841360","article-title":"A Utility-Optimized Framework for Personalized Private Histogram Estimation","volume":"31","author":"NIE","year":"2019","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_58","unstructured":"Murakami, T., and Kawamoto, Y. (2019, January 14\u201316). Utility-optimized local differential privacy mechanisms for distribution estimation. Proceedings of the USENIX Security Symposium, Santa Clara, CA, USA."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Gu, X., Li, M., Xiong, L., and Cao, Y. (2020, January 20\u201324). Providing Input-Discriminative Protection for Local Differential Privacy. Proceedings of the 2020 IEEE 36th International Conference on Data Engineering (ICDE), Dallas, TX, USA.","DOI":"10.1109\/ICDE48307.2020.00050"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Takagi, S., Cao, Y., and Yoshikawa, M. (2020, January 5\u20139). POSTER: Data Collection via Local Differential Privacy with Secret Parameters. Proceedings of the 15th ACM Asia Conference on Computer and Communications Security, Taipei, Taiwan.","DOI":"10.1145\/3320269.3405441"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Bassily, R., and Smith, A. (2015, January 14\u201317). Local, private, efficient protocols for succinct histograms. Proceedings of the Forty-Seventh Annual ACM Symposium on Theory of Computing, Portland, OR, USA.","DOI":"10.1145\/2746539.2746632"},{"key":"ref_62","unstructured":"Wang, T., Zhao, J., Yang, X., and Ren, X. (2019). Locally differentially private data collection and analysis. arXiv."},{"key":"ref_63","unstructured":"Kairouz, P., Oh, S., and Viswanath, P. (2014). Extremal mechanisms for local differential privacy. Advances in Neural Information Processing Systems, Curran Associates, Inc."},{"key":"ref_64","unstructured":"Kairouz, P., Bonawitz, K., and Ramage, D. (2016, January 19\u201324). Discrete distribution estimation under local privacy. Proceedings of the International Conference on Machine Learning, New York, NY, USA."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Wang, S., Huang, L., Wang, P., Deng, H., Xu, H., and Yang, W. (2016). Private Weighted Histogram Aggregation in Crowdsourcing, Springer.","DOI":"10.1007\/978-3-319-42836-9_23"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"422","DOI":"10.1145\/362686.362692","article-title":"Space\/time trade-offs in hash coding with allowable errors","volume":"13","author":"Bloom","year":"1970","journal-title":"Commun. ACM"},{"key":"ref_67","unstructured":"Acharya, J., Sun, Z., and Zhang, H. (2019, January 16\u201318). Hadamard Response: Estimating Distributions Privately, Efficiently, and with Little Communication. Proceedings of the International Conference on Artificial Intelligence and Statistics, Naha, Okinawa, Japan."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"1126","DOI":"10.14778\/3339490.3339496","article-title":"Answering range queries under local differential privacy","volume":"12","author":"Cormode","year":"2019","journal-title":"Proc. VLDB Endow."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Wang, S., Huang, L., Wang, P., Nie, Y., Xu, H., Yang, W., Li, X.Y., and Qiao, C. (2016). Mutual information optimally local private discrete distribution estimation. arXiv.","DOI":"10.1109\/INFOCOM.2017.8056977"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"2046","DOI":"10.1109\/TPDS.2019.2899097","article-title":"Local Differential Private Data Aggregation for Discrete Distribution Estimation","volume":"30","author":"Wang","year":"2019","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Ren, X., Yu, C.M., Yu, W., Yang, S., Yang, X., and McCann, J. (2016, January 8\u201310). High-dimensional crowdsourced data distribution estimation with local privacy. Proceedings of the IEEE International Conference on Computer and Information Technology (CIT), Nadi, Fiji.","DOI":"10.1109\/CIT.2016.57"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"2151","DOI":"10.1109\/TIFS.2018.2812146","article-title":"LoPub: High-Dimensional Crowdsourced Data Publication with Local Differential Privacy","volume":"13","author":"Ren","year":"2018","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Yang, X., Wang, T., Ren, X., and Yu, W. (2017, January 4\u20138). Copula-Based Multi-Dimensional Crowdsourced Data Synthesis and Release with Local Privacy. Proceedings of the IEEE Global Communications Conference (GLOBECOM), Singapore.","DOI":"10.1109\/GLOCOM.2017.8253989"},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Wang, T., Li, N., He, S., and Chen, J. (2018, January 15\u201319). CALM: Consistent adaptive local marginal for marginal release under local differential privacy. Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security, Toronto, ON, Canada.","DOI":"10.1145\/3243734.3243742"},{"key":"ref_75","unstructured":"Wang, T., Li, N., and Jha, S. (2019). Locally differentially private heavy hitter identification. IEEE Trans. Dependable Secure Comput., 1\u201312."},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Wang, S., Huang, L., Nie, Y., Wang, P., Xu, H., and Yang, W. (2018, January 16\u201319). PrivSet: Set-Valued Data Analyses with Locale Differential Privacy. Proceedings of the IEEE INFOCOM 2018-IEEE Conference on Computer Communications, Honolulu, HI, USA.","DOI":"10.1109\/INFOCOM.2018.8486234"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"31435","DOI":"10.1109\/ACCESS.2019.2899099","article-title":"LDPart: Effective Location-Record Data Publication via Local Differential Privacy","volume":"7","author":"Zhao","year":"2019","journal-title":"IEEE Access"},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Mishra, N., and Sandler, M. (2006, January 26\u201328). Privacy via pseudorandom sketches. Proceedings of the Twenty-Fifth ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, Chicago, IL, USA.","DOI":"10.1145\/1142351.1142373"},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Hsu, J., Khanna, S., and Roth, A. (2012, January 9\u201313). Distributed private heavy hitters. Proceedings of the International Colloquium on Automata, Languages, and Programming, Warwick, UK.","DOI":"10.1007\/978-3-642-31594-7_39"},{"key":"ref_80","unstructured":"Bassily, R., Nissim, K., Stemmer, U., and Thakurta, A.G. (2017). Practical locally private heavy hitters. Advances in Neural Information Processing Systems, Curran Associates, Inc."},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Bun, M., Nelson, J., and Stemmer, U. (2018, January 10\u201315). Heavy hitters and the structure of local privacy. Proceedings of the ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems, Houston, TX, USA.","DOI":"10.1145\/3196959.3196981"},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Jia, J., and Gong, N.Z. (May, January 29). Calibrate: Frequency Estimation and Heavy Hitter Identification with Local Differential Privacy via Incorporating Prior Knowledge. Proceedings of the IEEE INFOCOM 2019\u2014IEEE Conference on Computer Communications, Paris, France.","DOI":"10.1109\/INFOCOM.2019.8737527"},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"686151","DOI":"10.1155\/2014\/686151","article-title":"Personalized privacy-preserving frequent itemset mining using randomized response","volume":"2014","author":"Sun","year":"2014","journal-title":"Sci. World J."},{"key":"ref_84","doi-asserted-by":"crossref","unstructured":"Wang, T., Li, N., and Jha, S. (2018, January 21\u201323). Locally differentially private frequent itemset mining. Proceedings of the IEEE Symposium on Security and Privacy (SP), San Francisco, CA, USA.","DOI":"10.1109\/SP.2018.00035"},{"key":"ref_85","unstructured":"Thakurta, A.G., Vyrros, A.H., Vaishampayan, U.S., Kapoor, G., Freudinger, J., Prakash, V.V., Legendre, A., and Duplinsky, S. (2017). Emoji Frequency Detection and Deep Link Frequency. (U.S. Patent 9,705,908)."},{"key":"ref_86","unstructured":"Tang, J., Korolova, A., Bai, X., Wang, X., and Wang, X. (2017). Privacy loss in apple\u2019s implementation of differential privacy on macos 10.12. arXiv."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1515\/popets-2016-0015","article-title":"Building a with the unknown: Privacy-preserving learning of associations and data dictionaries","volume":"2016","author":"Fanti","year":"2016","journal-title":"Priv. Enhancing Technol."},{"key":"ref_88","doi-asserted-by":"crossref","unstructured":"Wang, N., Xiao, X., Yang, Y., Hoang, T.D., Shin, H., Shin, J., and Yu, G. (2018, January 16\u201319). PrivTrie: Effective frequent term discovery under local differential privacy. Proceedings of the IEEE ICDE, Paris, France.","DOI":"10.1109\/ICDE.2018.00079"},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"479","DOI":"10.1109\/TKDE.2018.2885749","article-title":"Learning New Words from Keystroke Data with Local Differential Privacy","volume":"32","author":"Kim","year":"2020","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"Ye, Q., Hu, H., Meng, X., and Zheng, H. (2019, January 19\u201323). PrivKV: Key-Value Data Collection with Local Differential Privacy. Proceedings of the IEEE Symposium on Security and Privacy (SP), San Francisco, CA, USA.","DOI":"10.1109\/SP.2019.00018"},{"key":"ref_91","unstructured":"Nguy\u00ean, T.T., Xiao, X., Yang, Y., Hui, S.C., Shin, H., and Shin, J. (2016). Collecting and analyzing data from smart device users with local differential privacy. arXiv."},{"key":"ref_92","unstructured":"Sun, L., Zhao, J., Ye, X., Feng, S., Wang, T., and Bai, T. (2019). Conditional Analysis for Key-Value Data with Local Differential Privacy. arXiv."},{"key":"ref_93","unstructured":"Gu, X., Li, M., Cheng, Y., Xiong, L., and Cao, Y. (2019). PCKV: Locally Differentially Private Correlated Key-Value Data Collection with Optimized Utility. arXiv."},{"key":"ref_94","doi-asserted-by":"crossref","unstructured":"Wang, S., Nie, Y., Wang, P., Xu, H., Yang, W., and Huang, L. (2017, January 1\u20134). Local private ordinal data distribution estimation. Proceedings of the IEEE INFOCOM, Atlanta, GA, USA.","DOI":"10.1109\/INFOCOM.2017.8056977"},{"key":"ref_95","doi-asserted-by":"crossref","unstructured":"Yang, J., Cheng, X., Su, S., Chen, R., Ren, Q., and Liu, Y. (2019, January 8\u201311). Collecting Preference Rankings Under Local Differential Privacy. Proceedings of the IEEE ICDE, Macau, China.","DOI":"10.1109\/ICDE.2019.00151"},{"key":"ref_96","unstructured":"Black, D. (2012). The Theory of Committees and Elections, Springer Science & Business Media."},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1177\/0192512102023004002","article-title":"Social choice in the south seas: Electoral innovation and the borda count in the pacific island countries","volume":"23","author":"Reilly","year":"2002","journal-title":"Int. Political Sci. Rev."},{"key":"ref_98","doi-asserted-by":"crossref","unstructured":"Brandt, F., Conitzer, V., Endriss, U., Lang, J., and Procaccia, A.D. (2016). Handbook of Computational Social Choice, Cambridge University Press.","DOI":"10.1017\/CBO9781107446984.002"},{"key":"ref_99","unstructured":"Wang, S., Du, J., Yang, W., Diao, X., Liu, Z., Nie, Y., Huang, L., and Xu, H. (2019). Aggregating Votes with Local Differential Privacy: Usefulness, Soundness vs. Indistinguishability. arXiv."},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"341","DOI":"10.14778\/2732269.2732271","article-title":"A Data- and Workload-Aware Query Answering Algorithm for Range Queries Under Differential Privacy","volume":"7","author":"Li","year":"2014","journal-title":"Proc. VLDB Endow."},{"key":"ref_101","doi-asserted-by":"crossref","unstructured":"Alnemari, A., Romanowski, C.J., and Raj, R.K. (2017, January 23\u201326). An Adaptive Differential Privacy Algorithm for Range Queries over Healthcare Data. Proceedings of the IEEE International Conference on Healthcare Informatics, Park City, UT, USA.","DOI":"10.1109\/ICHI.2017.49"},{"key":"ref_102","doi-asserted-by":"crossref","unstructured":"Kulkarni, T. (July, January 30). Answering Range Queries Under Local Differential Privacy. Proceedings of the 2019 International Conference on Management of Data, Amsterdam, The Netherlands.","DOI":"10.1145\/3299869.3300102"},{"key":"ref_103","doi-asserted-by":"crossref","unstructured":"Gu, X., Li, M., Cao, Y., and Xiong, L. (2019, January 10\u201312). Supporting Both Range Queries and Frequency Estimation with Local Differential Privacy. Proceedings of the IEEE Conference on Communications and Network Security (CNS), Washington, DC, USA.","DOI":"10.1109\/CNS.2019.8802778"},{"key":"ref_104","doi-asserted-by":"crossref","unstructured":"Li, Z., Wang, T., Lopuha\u00e4-Zwakenberg, M., Li, N., and \u0160koric, B. (2020, January 14\u201319). Estimating Numerical Distributions under Local Differential Privacy. Proceedings of the ACM SIGMOD International Conference on Management of Data, Portland, OR, USA.","DOI":"10.1145\/3318464.3389700"},{"key":"ref_105","unstructured":"Wang, T., Yang, X., Ren, X., Yu, W., and Yang, S. (2019). Locally Private High-dimensional Crowdsourced Data Release based on Copula Functions. IEEE Trans. Serv. Comput., 1\u201314."},{"key":"ref_106","doi-asserted-by":"crossref","unstructured":"Qardaji, W., Yang, W., and Li, N. (2014, January 22\u201327). PriView: Practical differentially private release of marginal contingency tables. Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data, Snowbird, UT, USA.","DOI":"10.1145\/2588555.2588575"},{"key":"ref_107","unstructured":"Wang, T., Ding, B., Zhou, J., Hong, C., Huang, Z., Li, N., and Jha, S. (July, January 30). Answering multi-dimensional analytical queries under local differential privacy. Proceedings of the International Conference Management of Data, Amsterdam, The Netherlands."},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"1862","DOI":"10.14778\/3352063.3352085","article-title":"DPSAaS: Multi-Dimensional Data Sharing and Analytics as Services under Local Differential Privacy","volume":"12","author":"Xu","year":"2019","journal-title":"Proc. VLDB Endow."},{"key":"ref_109","unstructured":"Xue, Q., Zhu, Y., and Wang, J. (2019). Joint Distribution Estimation and Na\u00efve Bayes Classification under Local Differential Privacy. IEEE Trans. Emerg. Topics Comput., 1\u201311."},{"key":"ref_110","doi-asserted-by":"crossref","unstructured":"Cao, Y., Yoshikawa, M., Xiao, Y., and Xiong, L. (2017, January 19\u201322). Quantifying Differential Privacy under Temporal Correlations. Proceedings of the IEEE ICDE, San Diego, CA, USA.","DOI":"10.1109\/ICDE.2017.132"},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1016\/j.knosys.2017.02.004","article-title":"CTS-DP: Publishing correlated time-series data via differential privacy","volume":"122","author":"Wang","year":"2017","journal-title":"Knowl. Based Syst."},{"key":"ref_112","doi-asserted-by":"crossref","first-page":"1281","DOI":"10.1109\/TKDE.2018.2824328","article-title":"Quantifying Differential Privacy in Continuous Data Release Under Temporal Correlations","volume":"31","author":"Cao","year":"2019","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_113","unstructured":"Joseph, M., Roth, A., Ullman, J., and Waggoner, B. (2018). Local Differential Privacy for Evolving Data. Advances in Neural Information Processing Systems, Curran Associates, Inc."},{"key":"ref_114","doi-asserted-by":"crossref","unstructured":"Dwork, C., Naor, M., Pitassi, T., and Rothblum, G.N. (2010, January 5\u20138). Differential privacy under continual observation. Proceedings of the ACM Symposium on Theory of Computing, Cambridge, MA, USA.","DOI":"10.1145\/1806689.1806787"},{"key":"ref_115","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1080\/01621459.2017.1389735","article-title":"Minimax optimal procedures for locally private estimation","volume":"113","author":"Duchi","year":"2018","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_116","doi-asserted-by":"crossref","unstructured":"Shokri, R., Stronati, M., Song, C., and Shmatikov, V. (2017, January 22\u201326). Membership inference attacks against machine learning models. Proceedings of the IEEE Symposium on Security and Privacy (SP), San Jose, CA, USA.","DOI":"10.1109\/SP.2017.41"},{"key":"ref_117","unstructured":"Song, C., Ristenpart, T., and Shmatikov, V. (November, January 30). Machine learning models that remember too much. Proceedings of the ACM SIGSAC CCS, Dallas, TX, USA."},{"key":"ref_118","doi-asserted-by":"crossref","unstructured":"Fredrikson, M., Jha, S., and Ristenpart, T. (2015, January 12\u201316). Model inversion attacks that exploit confidence information and basic countermeasures. Proceedings of the ACM SIGSAC CCS, Denver, CO, USA.","DOI":"10.1145\/2810103.2813677"},{"key":"ref_119","doi-asserted-by":"crossref","unstructured":"Abadi, M., Chu, A., Goodfellow, I., McMahan, H.B., Mironov, I., Talwar, K., and Zhang, L. (2016, January 24\u201328). Deep learning with differential privacy. Proceedings of the ACM SIGSAC CCS, Vienna, Austria.","DOI":"10.1145\/2976749.2978318"},{"key":"ref_120","doi-asserted-by":"crossref","unstructured":"Phan, N., Wu, X., Hu, H., and Dou, D. (2017, January 18\u201321). Adaptive laplace mechanism: Differential privacy preservation in deep learning. Proceedings of the IEEE International Conference on Data Mining (ICDM), New Orleans, LA, USA.","DOI":"10.1109\/ICDM.2017.48"},{"key":"ref_121","doi-asserted-by":"crossref","unstructured":"Lee, J., and Kifer, D. (2018, January 19\u201323). Concentrated differentially private gradient descent with adaptive per-iteration privacy budget. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, London, UK.","DOI":"10.1145\/3219819.3220076"},{"key":"ref_122","doi-asserted-by":"crossref","first-page":"48901","DOI":"10.1109\/ACCESS.2019.2909559","article-title":"Differential Privacy Preservation in Deep Learning: Challenges, Opportunities and Solutions","volume":"7","author":"Zhao","year":"2019","journal-title":"IEEE Access"},{"key":"ref_123","unstructured":"Jayaraman, B., and Evans, D. (2019, January 14\u201316). Evaluating Differentially Private Machine Learning in Practice. Proceedings of the USENIX Security Symposium, Santa Clara, CA, USA."},{"key":"ref_124","unstructured":"Yilmaz, E., Al-Rubaie, M., and Chang, J.M. (2019). Locally differentially private naive bayes classification. arXiv."},{"key":"ref_125","unstructured":"Berrett, T., and Butucea, C. (2019). Classification under local differential privacy. arXiv."},{"key":"ref_126","doi-asserted-by":"crossref","unstructured":"Kung, S.Y. (2014). Kernel Methods and Machine Learning, Cambridge University Press.","DOI":"10.1017\/CBO9781139176224"},{"key":"ref_127","unstructured":"Nissim, K., Stemmer, U., and Vadhan, S. (July, January 26). Locating a small cluster privately. Proceedings of the ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, San Francisco, CA, USA."},{"key":"ref_128","doi-asserted-by":"crossref","unstructured":"Su, D., Cao, J., Li, N., Bertino, E., and Jin, H. (2016, January 9\u201311). Differentially Private K-Means Clustering. Proceedings of the ACM Conf. Data and Application Security and Privacy, New Orleans, LA, USA.","DOI":"10.1145\/2857705.2857708"},{"key":"ref_129","doi-asserted-by":"crossref","unstructured":"Feldman, D., Xiang, C., Zhu, R., and Rus, D. (2017, January 18\u201321). Coresets for differentially private k-means clustering and applications to privacy in mobile sensor networks. Proceedings of the ACM\/IEEE International Conference on Information Processing in Sensor Networks (IPSN), Pittsburgh, PA, USA.","DOI":"10.1145\/3055031.3055090"},{"key":"ref_130","unstructured":"Nissim, K., and Stemmer, U. (2018). Clustering Algorithms for the Centralized and Local Models. Algorithmic Learning Theory, Springer Verlag."},{"key":"ref_131","unstructured":"Sun, L., Zhao, J., and Ye, X. (2019). Distributed Clustering in the Anonymized Space with Local Differential Privacy. arXiv."},{"key":"ref_132","doi-asserted-by":"crossref","unstructured":"Karapiperis, D., Gkoulalas-Divanis, A., and Verykios, V.S. (2017, January 19\u201322). Distance-aware encoding of numerical values for privacy-preserving record linkage. Proceedings of the IEEE ICDE, San Diego, CA, USA.","DOI":"10.1109\/ICDE.2017.58"},{"key":"ref_133","doi-asserted-by":"crossref","first-page":"292","DOI":"10.1109\/TKDE.2017.2761759","article-title":"FEDERAL: A framework for distance-aware privacy-preserving record linkage","volume":"30","author":"Karapiperis","year":"2018","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_134","doi-asserted-by":"crossref","unstructured":"Li, Y., Liu, S., Wang, J., and Liu, M. (2017, January 27\u201330). A local-clustering-based personalized differential privacy framework for user-based collaborative filtering. Proceedings of the International Conference on Database Systems for Advanced Applications, Suzhou, China.","DOI":"10.1007\/978-3-319-55753-3_34"},{"key":"ref_135","doi-asserted-by":"crossref","unstructured":"Akter, M., and Hashem, T. (2017, January 3\u20135). Computing aggregates over numeric data with personalized local differential privacy. Proceedings of the Australasian Conference on Information Security and Privacy, Auckland, New Zealand.","DOI":"10.1007\/978-3-319-59870-3_14"},{"key":"ref_136","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.cose.2019.101699","article-title":"Distributed K-Means clustering guaranteeing local differential privacy","volume":"90","author":"Xia","year":"2020","journal-title":"Comput. Secur."},{"key":"ref_137","first-page":"1069","article-title":"Differentially private empirical risk minimization","volume":"12","author":"Chaudhuri","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_138","doi-asserted-by":"crossref","unstructured":"Smith, A., Thakurta, A., and Upadhyay, J. (2017, January 22\u201326). Is interaction necessary for distributed private learning?. Proceedings of the IEEE Symposium on Security and Privacy (SP), San Jose, CA, USA.","DOI":"10.1109\/SP.2017.35"},{"key":"ref_139","unstructured":"Zheng, K., Mou, W., and Wang, L. (2017, January 6\u201311). Collect at once, use effectively: Making non-interactive locally private learning possible. Proceedings of the International Conference on Machine Learning, Sydney, NSW, Australia."},{"key":"ref_140","unstructured":"Wang, D., Chen, C., and Xu, J. (2019, January 9\u201315). Differentially Private Empirical Risk Minimization with Non-convex Loss Functions. Proceedings of the International Conference on Machine Learning, Long Beach, CA, USA."},{"key":"ref_141","unstructured":"Wang, D., Zhang, H., Gaboardi, M., and Xu, J. (2019). Estimating Smooth GLM in Non-interactive Local Differential Privacy Model with Public Unlabeled Data. arXiv."},{"key":"ref_142","doi-asserted-by":"crossref","first-page":"296","DOI":"10.1016\/j.tcs.2019.12.019","article-title":"Principal component analysis in the local differential privacy model","volume":"809","author":"Wang","year":"2020","journal-title":"Theor. Comput. Sci."},{"key":"ref_143","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.jpdc.2019.09.009","article-title":"Privacy preserving classification on local differential privacy in data centers","volume":"135","author":"Fan","year":"2020","journal-title":"J. Parallel Distrib. Comput."},{"key":"ref_144","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1186\/s13673-019-0195-4","article-title":"Local privacy protection classification based on human-centric computing","volume":"9","author":"Yin","year":"2019","journal-title":"Hum.-Centric Comput. Inf. Sci."},{"key":"ref_145","unstructured":"Van der Hoeven, D. (2019). User-Specified Local Differential Privacy in Unconstrained Adaptive Online Learning. Advances in Neural Information Processing Systems, Curran Associates, Inc."},{"key":"ref_146","unstructured":"Jun, K.S., and Orabona, F. (2019). Parameter-Free Locally Differentially Private Stochastic Subgradient Descent. arXiv."},{"key":"ref_147","doi-asserted-by":"crossref","first-page":"4505","DOI":"10.1109\/JIOT.2020.2967734","article-title":"A hybrid deep learning architecture for privacy-preserving mobile analytics","volume":"7","author":"Osia","year":"2020","journal-title":"IEEE Internet Things J."},{"key":"ref_148","unstructured":"Zhao, J. (2018, January 2\u20137). Distributed Deep Learning under Differential Privacy with the Teacher-Student Paradigm. Proceedings of the Workshops of AAAI Conference on Artificial Intelligence, New Orleans, LA, USA."},{"key":"ref_149","doi-asserted-by":"crossref","first-page":"5140","DOI":"10.1109\/JIOT.2019.2897005","article-title":"EdgeSanitizer: Locally Differentially Private Deep Inference at the Edge for Mobile Data Analytics","volume":"6","author":"Xu","year":"2019","journal-title":"IEEE Internet Things J."},{"key":"ref_150","unstructured":"Pan, X., Wang, W., Zhang, X., Li, B., Yi, J., and Song, D. (2019, January 13\u201317). How you act tells a lot: Privacy-leaking attack on deep reinforcement learning. Proceedings of the International Conference on Autonomous Agents and MultiAgent Systems, Montreal, QC, Canada."},{"key":"ref_151","unstructured":"Gajane, P., Urvoy, T., and Kaufmann, E. (2018). Corrupt bandits for preserving local privacy. Algorithmic Learning Theory, Springer Verlag."},{"key":"ref_152","unstructured":"Basu, D., Dimitrakakis, C., and Tossou, A. (2019). Differential Privacy for Multi-armed Bandits: What Is It and What Is Its Cost?. arXiv."},{"key":"ref_153","unstructured":"Ono, H., and Takahashi, T. (2020). Locally Private Distributed Reinforcement Learning. arXiv."},{"key":"ref_154","unstructured":"Ren, W., Zhou, X., Liu, J., and Shroff, N.B. (2020). Multi-Armed Bandits with Local Differential Privacy. arXiv."},{"key":"ref_155","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1109\/MSP.2017.2743240","article-title":"Deep reinforcement learning: A brief survey","volume":"34","author":"Arulkumaran","year":"2017","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_156","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3339474","article-title":"Federated Machine Learning: Concept and Applications","volume":"10","author":"Yang","year":"2019","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"ref_157","first-page":"1","article-title":"Federated learning","volume":"13","author":"Yang","year":"2019","journal-title":"Synth. Lect. Artif. Intell. Mach. Learn."},{"key":"ref_158","first-page":"50","article-title":"Federated Learning: Challenges, Methods, and Future Directions","volume":"37","author":"Li","year":"2020","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_159","unstructured":"Li, Q., Wen, Z., and He, B. (2019). Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection. arXiv."},{"key":"ref_160","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1109\/MIS.2020.3010335","article-title":"Preserving User Privacy For Machine Learning: Local Differential Privacy or Federated Machine Learning","volume":"35","author":"Zheng","year":"2020","journal-title":"IEEE Intell. Syst."},{"key":"ref_161","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1177\/1550147720919698","article-title":"IFed: A novel federated learning framework for local differential privacy in Power Internet of Things","volume":"16","author":"Cao","year":"2020","journal-title":"Int. J. Distrib. Sens. Netw."},{"key":"ref_162","doi-asserted-by":"crossref","unstructured":"Seif, M., Tandon, R., and Li, M. (2020). Wireless federated learning with local differential privacy. arXiv.","DOI":"10.1109\/ISIT44484.2020.9174426"},{"key":"ref_163","unstructured":"Geyer, R.C., Klein, T., and Nabi, M. (2017). Differentially private federated learning: A client level perspective. arXiv."},{"key":"ref_164","doi-asserted-by":"crossref","first-page":"3454","DOI":"10.1109\/TIFS.2020.2988575","article-title":"Federated learning with differential privacy: Algorithms and performance analysis","volume":"15","author":"Wei","year":"2020","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_165","doi-asserted-by":"crossref","unstructured":"Truex, S., Liu, L., Chow, K.H., Gursoy, M.E., and Wei, W. (2020, January 27). LDP-Fed: Federated learning with local differential privacy. Proceedings of the ACM International Workshop on Edge Systems, Analytics and Networking, Heraklion, Greece.","DOI":"10.1145\/3378679.3394533"},{"key":"ref_166","unstructured":"Bhowmick, A., Duchi, J., Freudiger, J., Kapoor, G., and Rogers, R. (2018). Protection against reconstruction and its applications in private federated learning. arXiv."},{"key":"ref_167","unstructured":"Li, J., Khodak, M., Caldas, S., and Talwalkar, A. (2019). Differentially private meta-learning. arXiv."},{"key":"ref_168","doi-asserted-by":"crossref","unstructured":"Liu, R., Cao, Y., Yoshikawa, M., and Chen, H. (2020). FedSel: Federated SGD under Local Differential Privacy with Top-k Dimension Selection. arXiv.","DOI":"10.1007\/978-3-030-59410-7_33"},{"key":"ref_169","doi-asserted-by":"crossref","unstructured":"Sun, L., Qian, J., Chen, X., and Yu, P.S. (2020). LDP-FL: Practical Private Aggregation in Federated Learning with Local Differential Privacy. arXiv.","DOI":"10.24963\/ijcai.2021\/217"},{"key":"ref_170","unstructured":"Naseri, M., Hayes, J., and De Cristofaro, E. (2020). Toward Robustness and Privacy in Federated Learning: Experimenting with Local and Central Differential Privacy. arXiv."},{"key":"ref_171","unstructured":"(2019, May 10). Apple iOS Security. Available online: https:\/\/developer.apple.com\/documentation\/security."},{"key":"ref_172","doi-asserted-by":"crossref","first-page":"1998","DOI":"10.14778\/3352063.3352119","article-title":"SAP HANA goes private: From privacy research to privacy aware enterprise analytics","volume":"12","author":"Kessler","year":"2019","journal-title":"Proc. VLDB Endow."},{"key":"ref_173","doi-asserted-by":"crossref","first-page":"1125","DOI":"10.1109\/JIOT.2017.2683200","article-title":"A Survey on Internet of Things: Architecture, Enabling Technologies, Security and Privacy, and Applications","volume":"4","author":"Lin","year":"2017","journal-title":"IEEE Internet Things J."},{"key":"ref_174","doi-asserted-by":"crossref","unstructured":"Usman, M., Jan, M.A., and Puthal, D. (2020). PAAL: A Framework based on Authentication, Aggregation and Local Differential Privacy for Internet of Multimedia Things. IEEE Internet Things J., 7.","DOI":"10.1109\/JIOT.2019.2936512"},{"key":"ref_175","doi-asserted-by":"crossref","first-page":"5246","DOI":"10.1109\/JIOT.2020.2977220","article-title":"Singular Spectrum Analysis for Local Differential Privacy of Classifications in the Smart Grid","volume":"7","author":"Ou","year":"2020","journal-title":"IEEE Internet Things J."},{"key":"ref_176","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Zhao, J., Yang, M., Wang, T., Wang, N., Lyu, L., Niyato, D., and Lam, K.Y. (2020). Local differential privacy based federated learning for Internet of Things. arXiv.","DOI":"10.1109\/JIOT.2020.3037194"},{"key":"ref_177","doi-asserted-by":"crossref","unstructured":"Song, Z., Li, Z., and Chen, X. (2019, January 9\u201311). Local Differential Privacy Preserving Mechanism for Multi-attribute Data in Mobile Crowdsensing with Edge Computing. Proceedings of the IEEE International Conference on Smart Internet of Things (SmartIoT), Tianjin, China.","DOI":"10.1109\/SmartIoT.2019.00050"},{"key":"ref_178","unstructured":"Gaboardi, M., Lim, H.W., Rogers, R.M., and Vadhan, S.P. (2016, January 19\u201324). Differentially private chi-squared hypothesis testing: Goodness of fit and independence testing. Proceedings of the International Conference on Machine Learning, New York, NY, USA."},{"key":"ref_179","unstructured":"Cai, B., Daskalakis, C., and Kamath, G. (2017, January 6\u201311). Priv\u2019IT: Private and sample efficient identity testing. Proceedings of the International Conference on Machine Learning, Sydney, NSW, Australia."},{"key":"ref_180","unstructured":"Sheffet, O. (2017, January 6\u201311). Differentially private ordinary least squares. Proceedings of the International Conference on Machine Learning, Sydney, NSW, Australia."},{"key":"ref_181","doi-asserted-by":"crossref","unstructured":"Tong, X., Xi, B., Kantarcioglu, M., and Inan, A. (2017, January 19\u201321). Gaussian mixture models for classification and hypothesis tests under differential privacy. Proceedings of the IFIP Annual Conference on Data and Applications Security and Privacy, Philadelphia, PA, USA.","DOI":"10.1007\/978-3-319-61176-1_7"},{"key":"ref_182","first-page":"1","article-title":"Extremal Mechanisms for Local Differential Privacy","volume":"17","author":"Kairouz","year":"2016","journal-title":"J. Mach. Learn. Res."},{"key":"ref_183","unstructured":"Sheffet, O. (2018, January 10\u201315). Locally Private Hypothesis Testing. Proceedings of the International Conference on Machine Learning, Stockholm, Sweden."},{"key":"ref_184","unstructured":"Gaboardi, M., and Rogers, R. (2018, January 10\u201315). Local Private Hypothesis Testing: Chi-Square Tests. Proceedings of the International Conference on Machine Learning, Stockholm, Sweden."},{"key":"ref_185","unstructured":"Gaboardi, M., Rogers, R., and Sheffet, O. (2019, January 16\u201318). Locally Private Mean Estimation: Z-test and Tight Confidence Intervals. Proceedings of the International Conference on Artificial Intelligence and Statistics, Naha, Okinawa, Japan."},{"key":"ref_186","unstructured":"Acharya, J., Canonne, C., Freitag, C., and Tyagi, H. (2019, January 16\u201318). Test without Trust: Optimal Locally Private Distribution Testing. Proceedings of the International Conference on Artificial Intelligence and Statistics, Naha, Okinawa, Japan."},{"key":"ref_187","unstructured":"Qin, Z., Yu, T., Yang, Y., Khalil, I., Xiao, X., and Ren, K. (November, January 30). Generating synthetic decentralized social graphs with local differential privacy. Proceedings of the ACM SIGSAC CCS, Dallas, TX, USA."},{"key":"ref_188","unstructured":"Zhang, Y., Wei, J., Zhang, X., Hu, X., and Liu, W. (June, January 30). A two-phase algorithm for generating synthetic graph under local differential privacy. Proceedings of the International Conference on Communication and Network Security, Beijing, China."},{"key":"ref_189","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1016\/j.neucom.2018.11.104","article-title":"Local differential privacy for social network publishing","volume":"391","author":"Liu","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_190","doi-asserted-by":"crossref","unstructured":"Gao, T., Li, F., Chen, Y., and Zou, X. (2017, January 19\u201321). Preserving local differential privacy in online social networks. Proceedings of the International Conference on Wireless Algorithms, Systems, and Applications, Guilin, China.","DOI":"10.1007\/978-3-319-60033-8_35"},{"key":"ref_191","doi-asserted-by":"crossref","first-page":"1009","DOI":"10.1109\/TCSS.2018.2877045","article-title":"Local differential privately anonymizing online social networks under hrg-based model","volume":"5","author":"Gao","year":"2018","journal-title":"IEEE Trans. Comput. Soc. Syst."},{"key":"ref_192","doi-asserted-by":"crossref","unstructured":"Yang, J., Ma, X., Bai, X., and Cui, L. (2020, January 17\u201319). Graph Publishing with Local Differential Privacy for Hierarchical Social Networks. Proceedings of the IEEE International Conference on Electronics Information and Emergency Communication (ICEIEC), Beijing, China.","DOI":"10.1109\/ICEIEC49280.2020.9152325"},{"key":"ref_193","doi-asserted-by":"crossref","unstructured":"Ye, Q., Hu, H., Au, M.H., Meng, X., and Xiao, X. (2020, January 20\u201324). Towards Locally Differentially Private Generic Graph Metric Estimation. Proceedings of the IEEE ICDE, Dallas, TX, USA.","DOI":"10.1109\/ICDE48307.2020.00204"},{"key":"ref_194","doi-asserted-by":"crossref","unstructured":"Sun, H., Xiao, X., Khalil, I., Yang, Y., Qin, Z., Wang, H., and Yu, T. (2019, January 11\u201315). Analyzing subgraph statistics from extended local views with decentralized differential privacy. Proceedings of the ACM SIGSAC Conference on Computer and Communications Security, London, UK.","DOI":"10.1145\/3319535.3354253"},{"key":"ref_195","doi-asserted-by":"crossref","first-page":"3239","DOI":"10.1109\/TIFS.2020.2985524","article-title":"AsgLDP: Collecting and Generating Decentralized Attributed Graphs With Local Differential Privacy","volume":"15","author":"Wei","year":"2020","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_196","doi-asserted-by":"crossref","first-page":"1837","DOI":"10.1007\/s10586-017-1078-y","article-title":"Big data and rule-based recommendation system in Internet of Things","volume":"22","author":"Jeong","year":"2019","journal-title":"Clust. Comput."},{"key":"ref_197","doi-asserted-by":"crossref","unstructured":"Calandrino, J.A., Kilzer, A., Narayanan, A., Felten, E.W., and Shmatikov, V. (2011, January 22\u201325). You Might Also Like: Privacy Risks of Collaborative Filtering. Proceedings of the IEEE Symposium on Security and Privacy (SP), Berkeley, CA, USA.","DOI":"10.1109\/SP.2011.40"},{"key":"ref_198","first-page":"864","article-title":"When Privacy Meets Usability: Unobtrusive Privacy Permission Recommendation System for Mobile Apps Based on Crowdsourcing","volume":"11","author":"Liu","year":"2018","journal-title":"IEEE Trans. Serv. Comput."},{"key":"ref_199","doi-asserted-by":"crossref","first-page":"1770","DOI":"10.1109\/TKDE.2018.2805356","article-title":"Privacy enhanced matrix factorization for recommendation with local differential privacy","volume":"30","author":"Shin","year":"2018","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_200","doi-asserted-by":"crossref","first-page":"248","DOI":"10.1016\/j.ins.2018.12.085","article-title":"Towards a more reliable privacy-preserving recommender system","volume":"482","author":"Jiang","year":"2019","journal-title":"Inf. Sci."},{"key":"ref_201","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1016\/j.ins.2019.06.021","article-title":"Locally differentially private item-based collaborative filtering","volume":"502","author":"Guo","year":"2019","journal-title":"Inf. Sci."},{"key":"ref_202","doi-asserted-by":"crossref","unstructured":"Gao, C., Huang, C., Lin, D., Jin, D., and Li, Y. (2020, January 25\u201330). DPLCF: Differentially Private Local Collaborative Filtering. Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, Virtual Event, China.","DOI":"10.1145\/3397271.3401053"},{"key":"ref_203","doi-asserted-by":"crossref","unstructured":"Wang, S., Huang, L., Tian, M., Yang, W., Xu, H., and Guo, H. (2015, January 6\u201310). Personalized privacy-preserving data aggregation for histogram estimation. Proceedings of the IEEE Global Communications Conference (GLOBECOM), San Diego, CA, USA.","DOI":"10.1109\/GLOCOM.2015.7417364"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/24\/7030\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:42:27Z","timestamp":1760179347000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/24\/7030"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,12,8]]},"references-count":203,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2020,12]]}},"alternative-id":["s20247030"],"URL":"https:\/\/doi.org\/10.3390\/s20247030","relation":{"is-referenced-by":[{"id-type":"doi","id":"10.1038\/s41598-025-01873-8","asserted-by":"object"}]},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,12,8]]}}}