Many of the data we collect today can easily be linked to an individual, household or entity. Unfortunately, using data without protecting the identity of the data owner can lead to data leaks and potential lawsuits. To maintain user privacy when a publication of data occurs many databases employ anonymization techniques, either on the query results or the data itself. In this paper we examine variant of such technique, “data perturbation” and discuss its vulnerability. The data perturbation method deals with changing the values of records in the dataset while maintaining a level of accuracy over the resulting queries. We focus on a relatively new data perturbation method called NeNDS  and show a possible partial knowledge privacy attack on this method.
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Conference||5th International Symposium on Cyber Security Cryptography and Machine Learning, CSCML 2021|
|Period||8/07/21 → 9/07/21|
- Collaborative filtering