{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T15:31:58Z","timestamp":1772119918415,"version":"3.50.1"},"reference-count":19,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2017,3,5]],"date-time":"2017-03-05T00:00:00Z","timestamp":1488672000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>In recent years, smart home technologies have started to be widely used, bringing a great deal of convenience to people\u2019s daily lives. At the same time, privacy issues have become particularly prominent. Traditional encryption methods can no longer meet the needs of privacy protection in smart home applications, since attacks can be launched even without the need for access to the cipher. Rather, attacks can be successfully realized through analyzing the frequency of radio signals, as well as the timestamp series, so that the daily activities of the residents in the smart home can be learnt. Such types of attacks can achieve a very high success rate, making them a great threat to users\u2019 privacy. In this paper, we propose an adaptive method based on sample data analysis and supervised learning (SDASL), to hide the patterns of daily routines of residents that would adapt to dynamically changing network loads. Compared to some existing solutions, our proposed method exhibits advantages such as low energy consumption, low latency, strong adaptability, and effective privacy protection.<\/jats:p>","DOI":"10.3390\/fi9010007","type":"journal-article","created":{"date-parts":[[2017,3,9]],"date-time":"2017-03-09T06:56:43Z","timestamp":1489042603000},"page":"7","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["An Adaptive Privacy Protection Method for Smart Home Environments Using Supervised Learning"],"prefix":"10.3390","volume":"9","author":[{"given":"Jingsha","family":"He","sequence":"first","affiliation":[{"name":"Faculty of Information Technology &amp; Beijing Engineering Research Center for IoT Software and Systems, Beijing University of Technology, Beijing 100124, China"},{"name":"College of Computer and Information Technology, China Three Gorges University, Yichang 443002, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9937-5264","authenticated-orcid":false,"given":"Qi","family":"Xiao","sequence":"additional","affiliation":[{"name":"Faculty of Information Technology &amp; Beijing Engineering Research Center for IoT Software and Systems, Beijing University of Technology, Beijing 100124, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peng","family":"He","sequence":"additional","affiliation":[{"name":"College of Computer and Information Technology, China Three Gorges University, Yichang 443002, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Muhammad","family":"Pathan","sequence":"additional","affiliation":[{"name":"Faculty of Information Technology &amp; Beijing Engineering Research Center for IoT Software and Systems, Beijing University of Technology, Beijing 100124, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2017,3,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1313","DOI":"10.1016\/j.engappai.2012.05.002","article-title":"State of the art of smart homes","volume":"25","author":"Morikawa","year":"2012","journal-title":"Eng. 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Protecting your daily in-home activity information from a wireless snooping attack. Proceedings of the 10th International Conference on Ubiquitous Computing, Seoul, Korea.","DOI":"10.1145\/1409635.1409663"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"16235","DOI":"10.3390\/s140916235","article-title":"Energy-Efficient Privacy Protection for Smart Home Environments Using Behavioral Semantics","volume":"14","author":"Park","year":"2014","journal-title":"Sensors"},{"key":"ref_7","first-page":"51","article-title":"Towards Statistically Strong Source Anonymity for Sensor Networks","volume":"9","author":"Yang","year":"2008","journal-title":"J. IEEE INFOCOM"},{"key":"ref_8","first-page":"1","article-title":"Toward a Statistical Framework for Source Anonymity in Sensor Networks","volume":"12","author":"Alomair","year":"2011","journal-title":"J. Mob. 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Prog."}],"container-title":["Future Internet"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-5903\/9\/1\/7\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T18:29:47Z","timestamp":1760207387000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-5903\/9\/1\/7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,3,5]]},"references-count":19,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2017,3]]}},"alternative-id":["fi9010007"],"URL":"https:\/\/doi.org\/10.3390\/fi9010007","relation":{},"ISSN":["1999-5903"],"issn-type":[{"value":"1999-5903","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,3,5]]}}}