Practical m-k-anonymization for collaborative data publishing without trusted third party

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 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.