{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T15:08:48Z","timestamp":1779203328156,"version":"3.51.4"},"reference-count":60,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2024,11,18]],"date-time":"2024-11-18T00:00:00Z","timestamp":1731888000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"NSF","award":["2238701"],"award-info":[{"award-number":["2238701"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Internet Technol."],"published-print":{"date-parts":[[2024,11,30]]},"abstract":"<jats:p>Internet of Things (IoT) devices have been increasingly deployed in smart homes to automatically monitor and control their environments. Unfortunately, extensive recent research has shown that on-path external adversaries can infer and further fingerprint people\u2019s sensitive private information by analyzing IoT network traffic traces. In addition, most recent approaches that aim to defend against these malicious IoT traffic analytics cannot adequately protect user privacy with reasonable traffic overhead. In particular, these approaches often did not consider practical traffic reshaping limitations, user daily routine permitting, and user privacy protection preference in their design. To address these issues, we design a new low-cost, open source user-centric defense system\u2014PrivacyGuard\u2014that enables people to regain the privacy leakage control of their IoT devices while still permitting sophisticated IoT data analytics that is necessary for smart home automation. In essence, our approach employs intelligent deep convolutional generative adversarial network assisted IoT device traffic signature learning, long short-term memory based artificial traffic signature injection, and partial traffic reshaping to obfuscate private information that can be observed in IoT device traffic traces. We evaluate PrivacyGuard using IoT network traffic traces of 31 IoT devices from five smart homes and buildings. We find that PrivacyGuard can effectively prevent a wide range of state-of-the-art adversarial machine learning and deep learning based user in-home activity inference and fingerprinting attacks and help users achieve the balance between their IoT data utility and privacy preserving.<\/jats:p>","DOI":"10.1145\/3701726","type":"journal-article","created":{"date-parts":[[2024,10,25]],"date-time":"2024-10-25T11:00:08Z","timestamp":1729854008000},"page":"1-33","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Safeguarding User-Centric Privacy in Smart Homes"],"prefix":"10.1145","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5224-1410","authenticated-orcid":false,"given":"Keyang","family":"Yu","sequence":"first","affiliation":[{"name":"Computer Science, Colorado School of Mines, Golden, United States"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6418-7474","authenticated-orcid":false,"given":"Qi","family":"Li","sequence":"additional","affiliation":[{"name":"Computer Science, Colorado School of Mines, Golden, United States"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1052-5658","authenticated-orcid":false,"given":"Dong","family":"Chen","sequence":"additional","affiliation":[{"name":"Computer Science, Colorado School of Mines, Golden, United States"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-7222-5507","authenticated-orcid":false,"given":"Liting","family":"Hu","sequence":"additional","affiliation":[{"name":"University of California Santa Cruz, Santa Cruz, United States"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,11,18]]},"reference":[{"key":"e_1_3_2_2_2","unstructured":"Amazon. n.d. Differential Privacy. Retrieved October 25 2024 from https:\/\/www.amazon.science\/tag\/differential-privacy"},{"key":"e_1_3_2_3_2","unstructured":"Ostinato. 2020. Ostinato: Packet Generator and Network Traffic Generator. Retrieved October 25 2024 from https:\/\/ostinato.org\/"},{"key":"e_1_3_2_4_2","article-title":"PrivacyGuard","author":"GitHub","year":"2021","unstructured":"GitHub. 2021. PrivacyGuard. Retrieved October 25, 2024 from https:\/\/github.com\/cyber-physical-systems\/PrivacyGuard","journal-title":"https:\/\/github.com\/cyber-physical-systems\/PrivacyGuard"},{"key":"e_1_3_2_5_2","article-title":"Differential Privacy Overview","author":"Apple","year":"2024","unstructured":"Apple. 2024. Differential Privacy Overview. Retrieved October 25, 2024 from https:\/\/www.apple.com\/privacy\/docs\/Differential_Privacy_Overview.pdf","journal-title":"https:\/\/www.apple.com\/privacy\/docs\/Differential_Privacy_Overview.pdf"},{"key":"e_1_3_2_6_2","article-title":"Use Differential Privacy","author":"Google Cloud","year":"2024","unstructured":"Google Cloud. 2024. Use Differential Privacy. Retrieved October 25, 2024 from https:\/\/cloud.google.com\/bigquery\/docs\/differential-privacy","journal-title":"https:\/\/cloud.google.com\/bigquery\/docs\/differential-privacy"},{"key":"e_1_3_2_7_2","first-page":"265","volume-title":"Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI \u201916)","author":"Abadi Mart\u00edn","year":"2016","unstructured":"Mart\u00edn Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, Manjunath Kudlur, Josh Levenberg, Rajat Monga, Sherry Moore, Derek G. Murray, Benoit Steiner, Paul Tucker, Vijay Vasudevan, Pete Warden, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng. 2016. TensorFlow: A system for large-scale machine learning. In Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI \u201916). 265\u2013283."},{"key":"e_1_3_2_8_2","unstructured":"Josephine Akosa. n.d. Predictive Accuracy: A Misleading Performance Measure for Highly Imbalanced Data. Paper 942-2017. Oklahoma State University."},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1145\/3374664.3375723"},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.2478\/popets-2019-0040"},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.1109\/IoTDI49375.2020.00015"},{"key":"e_1_3_2_12_2","article-title":"Now Those Privacy Rules Are Gone, This Is How ISPs Will Actually Sell Your Personal Data","author":"Brewster T.","year":"2017","unstructured":"T. Brewster. 2017. Now Those Privacy Rules Are Gone, This Is How ISPs Will Actually Sell Your Personal Data. Retrieved October 25, 2024 from https:\/\/www.forbes.com\/sites\/thomasbrewster\/2017\/03\/30\/fcc-privacy-rules-how-isps-will-actually-sell-your-data\/","journal-title":"https:\/\/www.forbes.com\/sites\/thomasbrewster\/2017\/03\/30\/fcc-privacy-rules-how-isps-will-actually-sell-your-data\/"},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.1145\/2660267.2660362"},{"key":"e_1_3_2_14_2","first-page":"208","volume-title":"Proceedings of the 2014 IEEE International Conference on Pervasive Computing and Communications","author":"Chen Dong","year":"2014","unstructured":"Dong Chen, David Irwin, Prashant Shenoy, and Jeannie Albrecht. 2014. Combined heat and privacy: Preventing occupancy detection from smart meters. In Proceedings of the 2014 IEEE International Conference on Pervasive Computing and Communications. 208\u2013215."},{"key":"e_1_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.1109\/SP40001.2021.00001"},{"key":"e_1_3_2_16_2","article-title":"Cohen\u2019s Kappa","author":"Wikipedia","unstructured":"Wikipedia. n.d. Cohen\u2019s Kappa. Retrieved October 25, 2024 from https:\/\/en.wikipedia.org\/wiki\/Cohen%27s_kappa","journal-title":"https:\/\/en.wikipedia.org\/wiki\/Cohen%27s_kappa"},{"key":"e_1_3_2_17_2","doi-asserted-by":"publisher","DOI":"10.1145\/3229565.3229567"},{"key":"e_1_3_2_18_2","unstructured":"DD-WRT. n.d. DD-WRT: a Linux based Alternative OpenSource Firmware. Retrieved October 25 2024 from https:\/\/dd-wrt.com\/support\/router-database\/"},{"key":"e_1_3_2_19_2","doi-asserted-by":"publisher","DOI":"10.1145\/3243734.3243865"},{"key":"e_1_3_2_20_2","doi-asserted-by":"publisher","DOI":"10.1007\/11787006_1"},{"key":"e_1_3_2_21_2","doi-asserted-by":"crossref","unstructured":"Cynthia Dwork Krishnaram Kenthapadi Frank McSherry Ilya Mironov and Moni Naor. 2006. Our data ourselves: Privacy via distributed noise generation. In Advances in Cryptology\u2014EUROCRYPT 2006. Lecture Notes in Computer Science Vol. 4004. Springer 486\u2013503.","DOI":"10.1007\/11761679_29"},{"key":"e_1_3_2_22_2","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2012.28"},{"key":"e_1_3_2_23_2","article-title":"ExpressVPN","author":"ExpressVPN","unstructured":"ExpressVPN. n.d. ExpressVPN. Retrieved October 25, 2024 from https:\/\/www.expressvpn.com\/","journal-title":"https:\/\/www.expressvpn.com\/"},{"key":"e_1_3_2_24_2","doi-asserted-by":"publisher","DOI":"10.1006\/jcss.1997.1504"},{"key":"e_1_3_2_25_2","doi-asserted-by":"publisher","DOI":"10.1109\/PERCOMW.2019.8730787"},{"key":"e_1_3_2_26_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICACT.2008.4493886"},{"key":"e_1_3_2_27_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-68884-4_10"},{"key":"e_1_3_2_28_2","doi-asserted-by":"publisher","DOI":"10.1109\/SP46214.2022.9833672"},{"key":"e_1_3_2_29_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-45744-4_2"},{"key":"e_1_3_2_30_2","doi-asserted-by":"publisher","DOI":"10.1109\/CNS.2019.8802686"},{"key":"e_1_3_2_31_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4842-2766-4_7"},{"key":"e_1_3_2_32_2","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM41043.2020.9155516"},{"key":"e_1_3_2_33_2","article-title":"Smart Devices Leaking Data to Tech Giants Raises New IoT Privacy Issues","author":"Lindsey Nicole","year":"2019","unstructured":"Nicole Lindsey. 2019. Smart Devices Leaking Data to Tech Giants Raises New IoT Privacy Issues. Retrieved October 25, 2024 from https:\/\/www.cpomagazine.com\/data-privacy\/smart-devices-leaking-data-to-tech-giants-raises-new-iot-privacy-issues\/","journal-title":"https:\/\/www.cpomagazine.com\/data-privacy\/smart-devices-leaking-data-to-tech-giants-raises-new-iot-privacy-issues\/"},{"key":"e_1_3_2_34_2","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2018.2799820"},{"key":"e_1_3_2_35_2","article-title":"Matthews Correlation Coefficient","author":"Wikipedia","unstructured":"Wikipedia. n.d. Matthews Correlation Coefficient. Retrieved October 25, 2024 from https:\/\/en.wikipedia.org\/wiki\/Matthews%_correlation%_coefficient","journal-title":"https:\/\/en.wikipedia.org\/wiki\/Matthews%_correlation%_coefficient"},{"key":"e_1_3_2_36_2","article-title":"Collection of User Data by ISPs and Telecom Providers, and Sharing with Third Parties","year":"2018","unstructured":"Mirimir. 2018. Collection of User Data by ISPs and Telecom Providers, and Sharing with Third Parties. Retrieved October 25, 2024 from https:\/\/www.ivpn.net\/blog\/collection-of-user-data-by-isps-and-telecom-providers-and-sharing-with-third-parties","journal-title":"https:\/\/www.ivpn.net\/blog\/collection-of-user-data-by-isps-and-telecom-providers-and-sharing-with-third-parties"},{"key":"e_1_3_2_37_2","unstructured":"Jon Brodkin. 2019. ISPs Lied to Congress to Spread Confusion about Encrypted DNS Mozilla Says. Retrieved October 25 2024 from https:\/\/arstechnica.com\/tech-policy\/2019\/11\/isps-lied-to-congress-to-spread-confusion-about-encrypted-dns-mozilla-says\/"},{"key":"e_1_3_2_38_2","doi-asserted-by":"publisher","DOI":"10.1145\/2665943.2665950"},{"key":"e_1_3_2_39_2","doi-asserted-by":"publisher","DOI":"10.1145\/3302505.3310073"},{"key":"e_1_3_2_40_2","doi-asserted-by":"publisher","DOI":"10.3390\/s140916235"},{"key":"e_1_3_2_41_2","article-title":"Pearson Correlation Coefficient","author":"Wikipedia","unstructured":"Wikipedia. n.d. Pearson Correlation Coefficient. Retrieved October 25, 2024 from https:\/\/en.wikipedia.org\/wiki\/Pearson_correlation_coefficient","journal-title":"https:\/\/en.wikipedia.org\/wiki\/Pearson_correlation_coefficient"},{"key":"e_1_3_2_42_2","doi-asserted-by":"crossref","unstructured":"Stjepan Picek Annelie Heuser Alan Jovic Shivam Bhasin and Francesco Regazzoni. 2019. The curse of class imbalance and conflicting metrics with machine learning for side-channel evaluations. IACR Transactions on Cryptographic Hardware and Embedded Systems 2019 1 (2019) 209\u2013237.","DOI":"10.46586\/tches.v2019.i1.209-237"},{"key":"e_1_3_2_43_2","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2020.3025988"},{"key":"e_1_3_2_44_2","article-title":"Unsupervised representation learning with deep convolutional generative adversarial networks","author":"Radford Alec","year":"2015","unstructured":"Alec Radford, Luke Metz, and Soumith Chintala. 2015. Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434 (2015).","journal-title":"arXiv preprint arXiv:1511.06434"},{"key":"e_1_3_2_45_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICMEW.2013.6618397"},{"key":"e_1_3_2_46_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCSS.2014.2307453"},{"key":"e_1_3_2_47_2","doi-asserted-by":"publisher","DOI":"10.1109\/TG.2020.2992282"},{"key":"e_1_3_2_48_2","doi-asserted-by":"publisher","DOI":"10.1007\/11863908_2"},{"key":"e_1_3_2_49_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2018.2866249"},{"key":"e_1_3_2_50_2","article-title":"Spearman\u2019s Rank Correlation Coefficient","author":"Wikipedia","unstructured":"Wikipedia. n.d. Spearman\u2019s Rank Correlation Coefficient. Retrieved October 25, 2024 from https:\/\/en.wikipedia.org\/wiki\/Spearman%27s_rank_correlation_coefficient","journal-title":"https:\/\/en.wikipedia.org\/wiki\/Spearman%27s_rank_correlation_coefficient"},{"key":"e_1_3_2_51_2","article-title":"Internet of Things Connected Devices Installed base Worldwide from 2015 to 2025 (in Billions)","year":"2016","unstructured":"Statista. 2016. Internet of Things Connected Devices Installed base Worldwide from 2015 to 2025 (in Billions). Retrieved October 25, 2024 from https:\/\/www.statista.com\/statistics\/471264\/iot-number-of-connected-devices-worldwide\/","journal-title":"https:\/\/www.statista.com\/statistics\/471264\/iot-number-of-connected-devices-worldwide\/"},{"key":"e_1_3_2_52_2","doi-asserted-by":"publisher","DOI":"10.1109\/PERCOM.2019.8767413"},{"key":"e_1_3_2_53_2","unstructured":"Karen Simonyan and Andrew Zisserman. n.d. Very Deep Convolutional Networks for Large-Scale Visual Recognition. Retrieved October 25 2024 from https:\/\/www.robots.ox.ac.uk\/ vgg\/research\/very_deep\/"},{"key":"e_1_3_2_54_2","doi-asserted-by":"publisher","DOI":"10.1145\/3395351.3399357"},{"key":"e_1_3_2_55_2","first-page":"143","volume-title":"Proceedings of the 23rd USENIX Security Symposium (USENIX Security \u201914)","author":"Wang Tao","year":"2014","unstructured":"Tao Wang, Xiang Cai, Rishab Nithyanand, Rob Johnson, and Ian Goldberg. 2014. Effective attacks and provable defenses for website fingerprinting. In Proceedings of the 23rd USENIX Security Symposium (USENIX Security \u201914). 143\u2013157."},{"key":"e_1_3_2_56_2","doi-asserted-by":"publisher","DOI":"10.1515\/popets-2016-0027"},{"key":"e_1_3_2_57_2","first-page":"1375","volume-title":"Proceedings of the 26th USENIX Security Symposium","author":"Wang Tao","year":"2017","unstructured":"Tao Wang and Ian Goldberg. 2017. Walkie-Talkie: An efficient defense against passive website fingerprinting attacks. In Proceedings of the 26th USENIX Security Symposium. 1375\u20131390."},{"key":"e_1_3_2_58_2","doi-asserted-by":"publisher","DOI":"10.1145\/1455770.1455812"},{"key":"e_1_3_2_59_2","doi-asserted-by":"publisher","DOI":"10.1145\/2810103.2813645"},{"key":"e_1_3_2_60_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2018.8461330"},{"key":"e_1_3_2_61_2","doi-asserted-by":"publisher","DOI":"10.1109\/MNET.2017.1500057NM"}],"container-title":["ACM Transactions on Internet Technology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3701726","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3701726","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T01:57:16Z","timestamp":1750298236000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3701726"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,18]]},"references-count":60,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2024,11,30]]}},"alternative-id":["10.1145\/3701726"],"URL":"https:\/\/doi.org\/10.1145\/3701726","relation":{},"ISSN":["1533-5399","1557-6051"],"issn-type":[{"value":"1533-5399","type":"print"},{"value":"1557-6051","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,18]]},"assertion":[{"value":"2023-03-30","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-10-16","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-11-18","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}