Practicalm-k-Anonymization for Collaborative Data Publishing without Trusted Third Party

Jingyu Hua,Kim-Kwang Raymond Choo,An Tang,Qingyun Pan,Hong Ding,Yizhi Ren

doi:10.1155/2017/9532163

Abstract

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.

Highlights

In today’s interconnected society, our sensitive personal data are increasingly stored in various databases belonging to different online service providers
6 is straightforward to draw: as the set Pji denotes the m providers contributing the most records in QIi and |QIi| − ∑mj=1 |TPji (QIi)| ≥ k, it is easy to infer that, after removing any m providers data from QIi, the number of remained records must be no less than k
The algorithm returns quasi identifier (QI) that consist of QI1, QI2, . . . , QIng

Summary

Introduction

In today’s interconnected society, our sensitive personal data are increasingly stored in various databases belonging to different online service providers. A small to medium sized online service provider may wish to mine user purchasing patterns in order to fine-tune their marketing strategy and improve sales. Such (data mining) task is likely to be outsourced to a third-party marketing company; the records in the online service provider’s database will be shared with the third-party. In such a scenario, the online service provider requires a privacy-preserving data publishing (i.e., sharing) approach to ensure that the data is shared without breaching user privacy. If the records to be published are owned by a single provider, the provider can run algorithms, such as [1, 2]

Methods

Results

Discussion

Conclusion