Visible to the public Enhanced p-Sensitive k-Anonymity Models for Achieving Better Privacy

TitleEnhanced p-Sensitive k-Anonymity Models for Achieving Better Privacy
Publication TypeConference Paper
Year of Publication2020
AuthorsWang, N., Song, H., Luo, T., Sun, J., Li, J.
Conference Name2020 IEEE/CIC International Conference on Communications in China (ICCC)
Date PublishedAug. 2020
ISBN Number978-1-7281-7327-6
Keywordsanonymity, clustering, Clustering algorithms, composability, data anonymity, Data models, data privacy, data publishing, data utility, enhanced anonymous models, enhanced p-sensitive k-anonymity models, faces, homogeneous attacks, Human Behavior, Metrics, one size fits all unified privacy protection level, Partitioning algorithms, personalized privacy protection, personalized protection characteristic, privacy, Privacy Requirements, pubcrawl, Publishing, resilience, Resiliency, Resists, sensitive attacks, sensitive groups, skew attacks

To our best knowledge, the p-sensitive k-anonymity model is a sophisticated model to resist linking attacks and homogeneous attacks in data publishing. However, if the distribution of sensitive values is skew, the model is difficult to defend against skew attacks and even faces sensitive attacks. In practice, the privacy requirements of different sensitive values are not always identical. The "one size fits all" unified privacy protection level may cause unnecessary information loss. To address these problems, the paper quantifies privacy requirements with the concept of IDF and concerns more about sensitive groups. Two enhanced anonymous models with personalized protection characteristic, that is, (p,aisg) -sensitive k-anonymity model and (pi,aisg)-sensitive k-anonymity model, are then proposed to resist skew attacks and sensitive attacks. Furthermore, two clustering algorithms with global search and local search are designed to implement our models. Experimental results show that the two enhanced models have outstanding advantages in better privacy at the expense of a little data utility.

Citation Keywang_enhanced_2020