Practicalm-k-Anonymization for Collaborative Data Publishing without Trusted Third Party
2017 ◽
Vol 2017
◽
pp. 1-10
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In collaborative data publishing (CDP), anm-adversary attack refers to a scenario where up tommalicious data providers collude to infer data records contributed by other providers. Existing solutions either rely on a trusted third party (TTP) or introduce expensive computation and communication overheads. In this paper, we present a practical distributedk-anonymization scheme,m-k-anonymization, designed to defend againstm-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.
2022 ◽
Vol 18
(1)
◽
pp. 1-26
2002 ◽
Vol 17
(6)
◽
pp. 749-756
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2019 ◽
Vol 9
(6)
◽
pp. 5376
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Keyword(s):
2013 ◽
pp. 283-301
Keyword(s):