Practical m-k-Anonymization for Collaborative Data Publishing without Trusted Third Party. (16th March 2017) - Hua, Jingyu; Tang, An; Pan, Qingyun; Choo, Kim-Kwang Raymond; Ding, Hong; Ren, Yizhi
Abstract : In collaborative data publishing (CDP), an m -adversary attack refers to a scenario where up to m malicious 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 distributed k -anonymization scheme, m -k -anonymization, designed to defend against m -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.