{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T09:35:23Z","timestamp":1768469723146,"version":"3.49.0"},"reference-count":36,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2020,3,17]],"date-time":"2020-03-17T00:00:00Z","timestamp":1584403200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2020,3,17]],"date-time":"2020-03-17T00:00:00Z","timestamp":1584403200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Wireless Com Network"],"published-print":{"date-parts":[[2020,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Human-centered computing in cloud, edge, and fog is one of the most concerning issues. Edge and fog nodes generate huge amounts of data continuously, and the analysis of these data provides valuable information. But they also increase privacy risks. The personal sensitive data may be disclosed by untrusted third-party service providers, and the current solutions to privacy protection are inefficient, costly. It is difficult to obtain available statistics. To solve these problems, we propose a local differential privacy sensitive data collection protocol in human-centered computing. Firstly, to maintain high data utility, the selection of the optimal number of hash functions and the mapping length is based on the size of the collected data. Secondly, we hash the sensitive data, add the appropriate Laplace noise to the client side, and send the reports to the server side. Thirdly, we construct the count sketch matrix to obtain privacy statistics on the server side. Finally, the utility of the proposed protocol is verified by synthetic datasets and a real dataset. The experimental results demonstrate that the protocol can achieve a balance between data utility and privacy protection.<\/jats:p>","DOI":"10.1186\/s13638-020-01675-8","type":"journal-article","created":{"date-parts":[[2020,3,17]],"date-time":"2020-03-17T13:02:47Z","timestamp":1584450167000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Local differential privacy for human-centered computing"],"prefix":"10.1186","volume":"2020","author":[{"given":"Xianjin","family":"Fang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qingkui","family":"Zeng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7666-1038","authenticated-orcid":false,"given":"Gaoming","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,3,17]]},"reference":[{"issue":"5","key":"1675_CR1","doi-asserted-by":"publisher","first-page":"1063","DOI":"10.1109\/TCSS.2019.2906925","volume":"6","author":"L Qi","year":"2019","unstructured":"L. Qi, Q. He, F. Chen, et al., Finding All You Need: Web APIs Recommendation in Web of Things Through Keywords Search. IEEE Trans. Comput. Soc. Syst. 6(5), 1063\u20131072 (2019)","journal-title":"IEEE Trans. Comput. Soc. Syst."},{"key":"1675_CR2","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1016\/j.jnca.2019.02.008","volume":"133","author":"X Xu","year":"2019","unstructured":"X. Xu, Y. Li, T. Huang, et al., An energy-aware computation offloading method for smart edge computing in wireless metropolitan area networks. J. Net. Comput. Appl. 133, 75\u201385 (2019)","journal-title":"J. Net. Comput. Appl."},{"key":"1675_CR3","doi-asserted-by":"publisher","first-page":"522","DOI":"10.1016\/j.future.2018.12.055","volume":"95","author":"X Xu","year":"2019","unstructured":"X. Xu, Q. Liu, Y. Luo, et al., A computation offloading method over big data for IoT-enabled cloud-edge computing. Future Generation Comput. Syst. 95, 522\u2013533 (2019)","journal-title":"Future Generation Comput. Syst."},{"key":"1675_CR4","doi-asserted-by":"publisher","unstructured":"L. Qi, Y. Chen, Y. Yuan, et al., A QoS-aware virtual machine scheduling method for energy conservation in cloud-based cyber-physical systems. World Wide Web, 1\u201323 (2019). https:\/\/doi.org\/10.1007\/s11280-019-00684-y","DOI":"10.1007\/s11280-019-00684-y"},{"key":"1675_CR5","doi-asserted-by":"publisher","unstructured":"Y. Zhang, G. Cui, S. Deng, et al., Efficient Query of Quality Correlation for Service Composition. IEEE Trans.Serv. Comput. (2018). https:\/\/doi.org\/10.1109\/TSC.2018.2830773","DOI":"10.1109\/TSC.2018.2830773"},{"key":"1675_CR6","doi-asserted-by":"publisher","unstructured":"Y. Zhang, K. Wang, Q. He, et al., Covering-based Web Service Quality Prediction via Neighborhood-aware Matrix Factorization. IEEE Trans. Serv. Comput. (2019). https:\/\/doi.org\/10.1109\/TSC.2019.2891517","DOI":"10.1109\/TSC.2019.2891517"},{"key":"1675_CR7","doi-asserted-by":"publisher","unstructured":"Y. Zhang, C. Yin, Q. Wu, et al., Location-Aware Deep Collaborative Filtering for Service Recommendation. IEEE Transactions on Systems, Man, and Cybernetics. Systems (2019). https:\/\/doi.org\/10.1109\/TSMC.2019.2931723","DOI":"10.1109\/TSMC.2019.2931723"},{"key":"1675_CR8","doi-asserted-by":"publisher","unstructured":"X. Xu, Q. Liu, X. Zhang, et al., A blockchain-powered crowdsourcing method with privacy preservation in mobile environment. IEEE Trans. Comput. Soc. Syst. 6(6), 1407\u20131419 (2019). https:\/\/doi.org\/10.1109\/TCSS.2019.2909137","DOI":"10.1109\/TCSS.2019.2909137"},{"key":"1675_CR9","doi-asserted-by":"publisher","first-page":"636","DOI":"10.1016\/j.future.2018.02.050","volume":"88","author":"L Qi","year":"2018","unstructured":"L. Qi, X. Zhang, W. Dou, et al., A two-stage locality-sensitive hashing based approach for privacy-preserving mobile service recommendation in cross-platform edge environment. Future Generation Comp. Syst. 88, 636\u2013643 (2018)","journal-title":"Future Generation Comp. Syst."},{"key":"1675_CR10","doi-asserted-by":"crossref","unstructured":"C. Dwork, F. McSherry, K. Nissim, et al., in Theory of Cryptography Conference. Calibrating noise to sensitivity in private data analysis, 265\u2013284 (Springer, 2006)","DOI":"10.1007\/11681878_14"},{"key":"1675_CR11","doi-asserted-by":"publisher","first-page":"403","DOI":"10.1257\/pandp.20191107","volume":"109","author":"S Ruggles","year":"2019","unstructured":"S. Ruggles, C. Fitch, D. Magnuson, et al., Differential privacy and census data: implications for social and economic research. AEA Pap Proc. 109, 403\u2013408 (2019). https:\/\/doi.org\/10.1257\/pandp.20191107","journal-title":"AEA Pap Proc."},{"key":"1675_CR12","unstructured":"Facebook\u2019s privacy problems: a roundup, https:\/\/www.theguardian.com\/technology\/2018\/dec\/14\/facebook-privacy-problems-roundup. Accessed 10 Oct 2019."},{"key":"1675_CR13","unstructured":"\u2018The Snappening\u2019 Is Real: 90,000 Private Photos and 9,000 Hacked Snapchat Videos Leak Online, https:\/\/www.thedailybeast.com\/the-snappening-is-real-90000-private-photos-and-9000-hacked-snapchat-videos-leak-online?ref=scroll. Accessed 10 Oct 2019."},{"key":"1675_CR14","doi-asserted-by":"crossref","unstructured":"N. Wang, X. Xiao, Y. Yang, et al., Collecting and Analyzing Multidimensional Data with Local Differential Privacy. 2019 IEEE 35th Int. Conf. Data Eng (ICDE), 638\u2013649. IEEE (2019)","DOI":"10.1109\/ICDE.2019.00063"},{"key":"1675_CR15","doi-asserted-by":"crossref","unstructured":"\u00da. Erlingsson, V. Pihur, A. Korolova, Rappor: Randomized Aggregatable Privacy-Preserving Ordinal Response. Proc. 2014 ACM SIGSAC Conf. Comput. Commun Secur - CCS '14, 1054\u20131067 (2014)","DOI":"10.1145\/2660267.2660348"},{"key":"1675_CR16","unstructured":"Differential Privacy Team, Apple, Learning with Privacy at Scale. (2016)"},{"key":"1675_CR17","first-page":"06361","volume":"1905","author":"ME Gursoy","year":"2019","unstructured":"M.E. Gursoy, A. Tamersoy, S. Truex, et al., Secure and Utility-Aware Data Collection with Condensed Local Differential Privacy. arXiv preprint arXiv 1905, 06361 (2019)","journal-title":"arXiv preprint arXiv"},{"issue":"1","key":"1675_CR18","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1016\/j.jalgor.2003.12.001","volume":"55","author":"G Cormode","year":"2005","unstructured":"G. Cormode, S. Muthukrishnan, An improved data stream summary: the count-min sketch and its applications. J. Algorithms 55(1), 58\u201375 (2005). https:\/\/doi.org\/10.1016\/j.jalgor.2003.12.001","journal-title":"J. Algorithms"},{"issue":"3","key":"1675_CR19","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1515\/popets-2016-0015","volume":"2016","author":"G Fanti","year":"2016","unstructured":"G. Fanti, V. Pihur, \u00da. Erlingsson, Building a RAPPOR with the unknown: Privacy-preserving learning of associations and data dictionaries. Proc. Privacy Enhancing Technol. 2016(3), 41\u201361 (2016). https:\/\/doi.org\/10.1515\/popets-2016-0015","journal-title":"Proc. Privacy Enhancing Technol."},{"key":"1675_CR20","unstructured":"B. Ding, J. Kulkarni, S. Yekhanin, Collecting telemetry data privately. Adv. Neural Inf. Process. Syst., 3571\u20133580 (2017)"},{"key":"1675_CR21","doi-asserted-by":"crossref","unstructured":"T. Wang, B. Ding, J. Zhou, et al., Answering Multi-Dimensional Analytical Queries under Local Differential Privacy. Proc. 2019 Int. Conf. Manage. Data - SIGMOD '19, 159\u2013176 (2019)","DOI":"10.1145\/3299869.3319891"},{"key":"1675_CR22","unstructured":"T. Wang, J. Blocki, N. Li, et al., Locally differentially private protocols for frequency estimation. In, 26th USENIX Secur. Symp., 729\u2013745 (2017)"},{"key":"1675_CR23","first-page":"04705","volume":"1802","author":"J Acharya","year":"2018","unstructured":"J. Acharya, Z. Sun, H. Zhang, Hadamard response: Estimating distributions privately, efficiently, and with little communication. arXiv preprint arXiv 1802, 04705 (2018)","journal-title":"arXiv preprint arXiv"},{"key":"1675_CR24","unstructured":"M. Joseph, A. Roth, J. Ullman, et al., Local differential privacy for evolving data. In, Adv. Neural Inf. Process. Syst., 2375\u20132384 (2018)"},{"key":"1675_CR25","first-page":"08320","volume":"1905","author":"T Wang","year":"2019","unstructured":"T. Wang, Z. Li, N. Li, et al., Consistent and accurate frequency oracles under local differential privacy. arXiv preprint arXiv 1905, 08320 (2019)","journal-title":"arXiv preprint arXiv"},{"key":"1675_CR26","doi-asserted-by":"publisher","unstructured":"L. Qi, X. Zhang, S. Li, et al., Spatial-temporal data-driven service recommendation with privacy-preservation. Inform. Sci. 515, 91\u2013102 (2019). https:\/\/doi.org\/10.1016\/j.ins.2019.11.021","DOI":"10.1016\/j.ins.2019.11.021"},{"key":"1675_CR27","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1016\/j.future.2019.01.012","volume":"96","author":"X Xu","year":"2019","unstructured":"X. Xu, Y. Xue, L. Qi, et al., An edge computing-enabled computation offloading method with privacy preservation for internet of connected vehicles. Future Generation Comput. Syst. 96, 89\u2013100 (2019)","journal-title":"Future Generation Comput. Syst."},{"key":"1675_CR28","doi-asserted-by":"crossref","unstructured":"R. Bassily, A. Smith, Local, private, efficient protocols for succinct histograms. In, Proc. Forty-seventh Annu. ACM Symp. Theory Comput., 127\u2013135. ACM (2015)","DOI":"10.1145\/2746539.2746632"},{"issue":"9","key":"1675_CR29","doi-asserted-by":"publisher","first-page":"2151","DOI":"10.1109\/TIFS.2018.2812146","volume":"13","author":"X Ren","year":"2018","unstructured":"X. Ren, C.-M. Yu, W. Yu, et al., LoPub: High-Dimensional Crowdsourced Data Publication with Local Differential Privacy. IEEE Trans. Inform. Forensics Secur. 13(9), 2151\u20132166 (2018)","journal-title":"IEEE Trans. Inform. Forensics Secur."},{"key":"1675_CR30","first-page":"11515","volume":"1908","author":"T Wang","year":"2019","unstructured":"T. Wang, M. Xu, B. Ding, et al., Practical and Robust Privacy Amplification with Multi-Party Differential Privacy. arXiv preprint arXiv 1908, 11515 (2019)","journal-title":"arXiv preprint arXiv"},{"key":"1675_CR31","first-page":"12834","volume":"1911","author":"X Gu","year":"2019","unstructured":"X. Gu, M. Li, Y. Cheng, et al., PCKV: Locally Differentially Private Correlated Key-Value Data Collection with Optimized Utility. arXiv preprint arXiv 1911, 12834 (2019)","journal-title":"arXiv preprint arXiv"},{"key":"1675_CR32","doi-asserted-by":"crossref","unstructured":"K. Nissim, S. Raskhodnikova, A. Smith, Smooth sensitivity and sampling in private data analysis. In, Proc. Thirty-ninth Annu. ACM Symp Theory Comput., 75\u201384. ACM (2007)","DOI":"10.1145\/1250790.1250803"},{"key":"1675_CR33","unstructured":"Y. Wang, X. Wu, D. Hu, in EDBT\/ICDT Workshops. Using Randomized Response for Differential Privacy Preserving Data Collection, 1558\u20132016 (2016)."},{"issue":"3-4","key":"1675_CR34","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1561\/0400000042","volume":"9","author":"A Roth","year":"2014","unstructured":"A. Roth, C. Dwork, The algorithmic foundations of differential privacy. Foundations and Trends\u00ae in. Theor. Comput. Sci. 9(3-4), 211\u2013407 (2014). https:\/\/doi.org\/10.1561\/0400000042","journal-title":"Theor. Comput. Sci."},{"issue":"309","key":"1675_CR35","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1080\/01621459.1965.10480775","volume":"60","author":"SL Warner","year":"1965","unstructured":"S.L. Warner, Randomized response: a survey technique for eliminating evasive answer bias. J. Am. Stat. Assoc. 60(309), 63\u201369 (1965). https:\/\/doi.org\/10.1080\/01621459.1965.10480775","journal-title":"J. Am. Stat. Assoc."},{"key":"1675_CR36","volume-title":"Ronald Goeken, Josiah Grover, Erin Meyer, Jose Pacas and Matthew Sobek.: IPUMS USA: Version 9.0","author":"SF Steven Ruggles","year":"2019","unstructured":"S.F. Steven Ruggles, Ronald Goeken, Josiah Grover, Erin Meyer, Jose Pacas and Matthew Sobek.: IPUMS USA: Version 9.0 (IPUMS, Minneapolis, MN, 2019)"}],"container-title":["EURASIP Journal on Wireless Communications and Networking"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s13638-020-01675-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1186\/s13638-020-01675-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s13638-020-01675-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,3,17]],"date-time":"2021-03-17T00:09:19Z","timestamp":1615939759000},"score":1,"resource":{"primary":{"URL":"https:\/\/jwcn-eurasipjournals.springeropen.com\/articles\/10.1186\/s13638-020-01675-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,3,17]]},"references-count":36,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2020,12]]}},"alternative-id":["1675"],"URL":"https:\/\/doi.org\/10.1186\/s13638-020-01675-8","relation":{},"ISSN":["1687-1499"],"issn-type":[{"value":"1687-1499","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,3,17]]},"assertion":[{"value":"22 December 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 February 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 March 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare that they have no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"65"}}