{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T14:03:50Z","timestamp":1753884230023,"version":"3.41.2"},"reference-count":28,"publisher":"World Scientific Pub Co Pte Ltd","issue":"15","funder":[{"DOI":"10.13039\/501100001809","name":"NSFC","doi-asserted-by":"crossref","award":["62176027"],"award-info":[{"award-number":["62176027"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Patt. Recogn. Artif. Intell."],"published-print":{"date-parts":[[2022,12,15]]},"abstract":"<jats:p> Collecting users\u2019 historical data such as movie watching and music listening, and mining frequent items from them, can improve the utility of smart services, but there is also a risk of compromising user privacy. Local differential privacy is a strict definition of privacy and has been widely used in various privacy-preserving data collection scenarios. However, the accuracy of existing locally differentially private frequent items mining methods decreases significantly with the increase in the dimensions of data to be collected. In this paper, we propose a new locally differentially private frequent item mining method for high-dimensional data, which decreases the dimension used for data perturbation by grouping the contents and improving the interference matrix generation method, so as to improve the data reconstruction accuracy. The experimental results show that our proposed method can significantly improve the accuracy of frequent item mining and provide a better trade-off between privacy and accuracy compared with existing methods. <\/jats:p>","DOI":"10.1142\/s021800142259039x","type":"journal-article","created":{"date-parts":[[2022,10,5]],"date-time":"2022-10-05T14:18:03Z","timestamp":1664979483000},"source":"Crossref","is-referenced-by-count":0,"title":["Locally Differentially Private Frequent Pattern Mining for High-Dimensional Data in Mobile Smart Services"],"prefix":"10.1142","volume":"36","author":[{"given":"Qi","family":"Li","sequence":"first","affiliation":[{"name":"Big Data Research Center, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, P. R. China"},{"name":"School of Finance, Chongqing Technology and Business University, Chongqing 400030, P. R. China"}]},{"given":"Shunshun","family":"Peng","sequence":"additional","affiliation":[{"name":"College of Computer and Information Science, Chongqing Normal University, Chongqing 401331, P. R. China"}]},{"given":"Haonan","family":"Wu","sequence":"additional","affiliation":[{"name":"College of Computer and Information Science, Chongqing Normal University, Chongqing 401331, P. R. China"}]},{"given":"Ruisheng","family":"Ran","sequence":"additional","affiliation":[{"name":"College of Computer and Information Science, Chongqing Normal University, Chongqing 401331, P. R. China"}]},{"given":"Yong","family":"Li","sequence":"additional","affiliation":[{"name":"College of Computer Science, Chongqing University, Chongqing 400044, P. R. China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1874-3641","authenticated-orcid":false,"given":"Mingliang","family":"Zhou","sequence":"additional","affiliation":[{"name":"College of Computer Science, Chongqing University, Chongqing 400044, P. R. China"}]},{"given":"Taolin","family":"Guo","sequence":"additional","affiliation":[{"name":"College of Computer Science, Chongqing University, Chongqing 400044, P. R. China"}]},{"given":"Qin","family":"Mao","sequence":"additional","affiliation":[{"name":"Qiannan Normal Coll Nationalities, Coll Comp & Informat, Doupengshan Rd, Duyun 558000, P. R. China"},{"name":"Key Laboratory of Complex Systems and Intelligent Optimization of Guizhou Province, Duyun 558000, P. R. China"}]}],"member":"219","published-online":{"date-parts":[[2022,11,24]]},"reference":[{"key":"S021800142259039XBIB001","first-page":"51","volume-title":"Proc. 36th Int. Conf. Machine Learning","volume":"97","author":"Acharya J.","year":"2019"},{"key":"S021800142259039XBIB002","first-page":"232","volume-title":"33rd Int. Conf. Scientific and Statistical Database Management","author":"Afrose S.","year":"2021"},{"key":"S021800142259039XBIB003","first-page":"47","volume-title":"30th ACM Int. 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