{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T18:02:08Z","timestamp":1773511328241,"version":"3.50.1"},"reference-count":33,"publisher":"Association for Computing Machinery (ACM)","issue":"12","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2014,8]]},"abstract":"<jats:p>\n            Numerous applications require\n            <jats:italic>continuous<\/jats:italic>\n            publication of statistics or monitoring purposes, such as real-time traffic analysis, timely disease outbreak discovery, and social trends observation. These statistics may be derived from sensitive user data and, hence, necessitate privacy preservation. A notable paradigm for offering strong privacy guarantees in statistics publishing is \u03b5-differential privacy. However, there is limited literature that adapts this concept to settings where the statistics are computed over an\n            <jats:italic>infinite stream<\/jats:italic>\n            of \"events\" (i.e., data items generated by the users), and published periodically. These works aim at hiding a\n            <jats:italic>single<\/jats:italic>\n            event over the entire stream. We argue that, in most practical scenarios, sensitive information is revealed from\n            <jats:italic>multiple<\/jats:italic>\n            events occurring at\n            <jats:italic>contiguous<\/jats:italic>\n            time instances. Towards this end, we put forth the novel notion of\n            <jats:italic>w<\/jats:italic>\n            -\n            <jats:italic>event privacy<\/jats:italic>\n            over infinite streams, which protects any\n            <jats:italic>event sequence<\/jats:italic>\n            occurring in\n            <jats:italic>w<\/jats:italic>\n            successive time instants. We first formulate our privacy concept, motivate its importance, and introduce a methodology for achieving it. We next design two instantiations, whose utility is independent of the stream length. Finally, we confirm the practicality of our solutions experimenting with real data.\n          <\/jats:p>","DOI":"10.14778\/2732977.2732989","type":"journal-article","created":{"date-parts":[[2015,5,12]],"date-time":"2015-05-12T15:37:52Z","timestamp":1431445072000},"page":"1155-1166","source":"Crossref","is-referenced-by-count":200,"title":["Differentially private event sequences over infinite streams"],"prefix":"10.14778","volume":"7","author":[{"given":"Georgios","family":"Kellaris","sequence":"first","affiliation":[{"name":"HKUST"}]},{"given":"Stavros","family":"Papadopoulos","sequence":"additional","affiliation":[{"name":"Intel Labs \/ MIT"}]},{"given":"Xiaokui","family":"Xiao","sequence":"additional","affiliation":[{"name":"Nanyang Technological University"}]},{"given":"Dimitris","family":"Papadias","sequence":"additional","affiliation":[{"name":"HKUST"}]}],"member":"320","published-online":{"date-parts":[[2014,8]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"HCUP net. http:\/\/hcupnet.ahrq.gov\/.  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