{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,5,15]],"date-time":"2024-05-15T07:54:06Z","timestamp":1715759646516},"reference-count":20,"publisher":"Association for Computing Machinery (ACM)","issue":"12","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2018,8]]},"abstract":"<jats:p>\n            In many real-world systems, such as Internet of Thing, sensitive data streams are collected and analyzed continually. To protect privacy, a number of mechanisms are designed to achieve \u03f5-differential privacy for processing sensitive streaming data, whose privacy loss is considered to be rigorously controlled within a given parameter \u03f5\n            <jats:italic>.<\/jats:italic>\n            However, most of the existing studies do not consider the effect of temporal correlations among the continuously generated data on the privacy loss. Our recent work reveals that, the privacy loss of a traditional DP mechanism (e.g., Laplace mechanism) may not be bounded by \u03f5 due to temporal correlations. We call such unexpected privacy loss\n            <jats:italic>Temporal Privacy Leakage<\/jats:italic>\n            (TPL). In this demonstration, we design a system,\n            <jats:italic>ConTPL,<\/jats:italic>\n            which is able to automatically convert an existing differentially private streaming data release mechanism into one bounding TPL within a specified level. ConTPL also provides an interactive interface and real-time visualization to help data curator to understand and explore the effect of different parameters on TPL.\n          <\/jats:p>","DOI":"10.14778\/3229863.3236267","type":"journal-article","created":{"date-parts":[[2018,9,10]],"date-time":"2018-09-10T12:12:28Z","timestamp":1536581548000},"page":"2090-2093","source":"Crossref","is-referenced-by-count":3,"title":["ConTPL"],"prefix":"10.14778","volume":"11","author":[{"given":"Yang","family":"Cao","sequence":"first","affiliation":[{"name":"Emory University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Li","family":"Xiong","sequence":"additional","affiliation":[{"name":"Emory University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Masatoshi","family":"Yoshikawa","sequence":"additional","affiliation":[{"name":"Kyoto University, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yonghui","family":"Xiao","sequence":"additional","affiliation":[{"name":"Google Inc."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Si","family":"Zhang","sequence":"additional","affiliation":[{"name":"University of Calgary, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2018,8]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/2623330.2623361"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/2448496.2448530"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/MDM.2015.15"},{"key":"e_1_2_1_4_1","volume-title":"E99-D(1)","author":"Cao Y.","year":"2016"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2017.132"},{"key":"e_1_2_1_6_1","volume-title":"Quantifying differential privacy in continuous data release under temporal correlations","author":"Cao Y."},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-31680-7_8"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/3133956.3134102"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1007\/11787006_1"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/1806689.1806787"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/2660267.2660348"},{"issue":"9","key":"e_1_2_1_12_1","first-page":"2094","article-title":"An adaptive approach to real-time aggregate monitoring with differential privacy","volume":"26","author":"Fan L.","year":"2014","journal-title":"IEEE TKDE"},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/2181196.2181199"},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.14778\/2732977.2732989"},{"key":"e_1_2_1_15_1","volume-title":"NDSS","author":"Supriyo C.","year":"2016"},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/2370216.2370421"},{"key":"e_1_2_1_17_1","volume-title":"NDSS","author":"Shi E.","year":"2011"},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/2723372.2747643"},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/1869790.1869807"},{"issue":"2","key":"e_1_2_1_20_1","first-page":"32","article-title":"a collaborative social networking service among user, location and trajectory","volume":"33","author":"Zheng Y.","year":"2010","journal-title":"IEEE Data Eng. Bull."}],"container-title":["Proceedings of the VLDB Endowment"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.14778\/3229863.3236267","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,28]],"date-time":"2022-12-28T10:08:15Z","timestamp":1672222095000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.14778\/3229863.3236267"}},"subtitle":["controlling temporal privacy leakage in differentially private continuous data release"],"short-title":[],"issued":{"date-parts":[[2018,8]]},"references-count":20,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2018,8]]}},"alternative-id":["10.14778\/3229863.3236267"],"URL":"https:\/\/doi.org\/10.14778\/3229863.3236267","relation":{},"ISSN":["2150-8097"],"issn-type":[{"value":"2150-8097","type":"print"}],"subject":[],"published":{"date-parts":[[2018,8]]}}}