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Existing solutions either rely on a trusted third party (TTP) or introduce expensive computation and communication overheads. In this paper, we present a practical distributed<mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M5\"><mml:mrow><mml:mi>k<\/mml:mi><\/mml:mrow><\/mml:math>-anonymization scheme,<mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M6\"><mml:mrow><mml:mi>m<\/mml:mi><\/mml:mrow><\/mml:math>-<mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M7\"><mml:mrow><mml:mi>k<\/mml:mi><\/mml:mrow><\/mml:math>-anonymization, designed to defend against<mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M8\"><mml:mrow><mml:mi>m<\/mml:mi><\/mml:mrow><\/mml:math>-adversary attacks without relying on any TTPs. We then prove its security in the semihonest adversary model and demonstrate how an extension of the scheme can also be proven secure in a stronger adversary model. We also evaluate its efficiency using a commonly used dataset.<\/jats:p>","DOI":"10.1155\/2017\/9532163","type":"journal-article","created":{"date-parts":[[2017,3,16]],"date-time":"2017-03-16T17:11:16Z","timestamp":1489684276000},"page":"1-10","source":"Crossref","is-referenced-by-count":6,"title":["Practical<mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M1\"><mml:mrow><mml:mi>m<\/mml:mi><\/mml:mrow><\/mml:math>-<mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M2\"><mml:mrow><mml:mi>k<\/mml:mi><\/mml:mrow><\/mml:math>-Anonymization for Collaborative Data Publishing without Trusted Third Party"],"prefix":"10.1155","volume":"2017","author":[{"given":"Jingyu","family":"Hua","sequence":"first","affiliation":[{"name":"State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210093, China"}]},{"given":"An","family":"Tang","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Technology, Nanjing 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