{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,3,29]],"date-time":"2022-03-29T21:42:05Z","timestamp":1648590125542},"reference-count":3,"publisher":"World Scientific Pub Co Pte Lt","issue":"04","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Wavelets Multiresolut Inf. Process."],"published-print":{"date-parts":[[2017,7]]},"abstract":"<jats:p> Differential privacy becomes a standard for evaluating the privacy protection performance for an algorithm these years. However, the definition of differential privacy seems not so easy to understand as the classical k-anonymity and etc. In this paper, we propose a new measure which is more comprehensible. Some properties of such measure are investigated and the relationship between our new definition and differential privacy is studied. <\/jats:p>","DOI":"10.1142\/s0219691317500333","type":"journal-article","created":{"date-parts":[[2017,4,17]],"date-time":"2017-04-17T07:01:10Z","timestamp":1492412470000},"page":"1750033","source":"Crossref","is-referenced-by-count":3,"title":["Probability comprehension of differential privacy for privacy protection algorithms: A new measure"],"prefix":"10.1142","volume":"15","author":[{"given":"Weilin","family":"Nie","sequence":"first","affiliation":[{"name":"Department of Mathematics, Huizhou University, Huizhou, Guangdong, P. R. China"}]},{"given":"Cheng","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Mathematics, Huizhou University, Huizhou, Guangdong, P. R. China"}]}],"member":"219","published-online":{"date-parts":[[2017,4,17]]},"reference":[{"key":"S0219691317500333BIB002","doi-asserted-by":"publisher","DOI":"10.1561\/0400000042"},{"key":"S0219691317500333BIB004","doi-asserted-by":"publisher","DOI":"10.1145\/1217299.1217302"},{"key":"S0219691317500333BIB007","doi-asserted-by":"publisher","DOI":"10.1142\/S0218488502001648"}],"container-title":["International Journal of Wavelets, Multiresolution and Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.worldscientific.com\/doi\/pdf\/10.1142\/S0219691317500333","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,8,6]],"date-time":"2019-08-06T13:58:31Z","timestamp":1565099911000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.worldscientific.com\/doi\/abs\/10.1142\/S0219691317500333"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,4,17]]},"references-count":3,"journal-issue":{"issue":"04","published-online":{"date-parts":[[2017,3,21]]},"published-print":{"date-parts":[[2017,7]]}},"alternative-id":["10.1142\/S0219691317500333"],"URL":"https:\/\/doi.org\/10.1142\/s0219691317500333","relation":{},"ISSN":["0219-6913","1793-690X"],"issn-type":[{"value":"0219-6913","type":"print"},{"value":"1793-690X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,4,17]]}}}