Probabilistic km-anonymity efficient anonymization of large set-valued datasets
Gergely Ács, Jagdish Prasad Achara, Claude Castelluccia · 2015
Set-valued dataset contains different types of items/values per individual, for example, visited locations, purchased goods, watched movies, or search queries. As it is relatively easy to re-identify individuals in such datasets, their release poses significant privacy threats. Hence, organizations aiming to share such datasets must adhere to personal data regulations. In order to get rid of these regulations and also to beneit from sharing, these datasets should be anonymized before their release. In this paper, we revisit the problem of anonymizing set-valued data. We argue that anonymization techniques targeting traditional km-anonymity model, which limits the adversarial background knowledge to at most m items per individual, are impractical for large real-world datasets. Hence, we propose a probabilistic relaxation of km-anonymity and present an anonymization technique to achieve it. This relaxation also improves the utility of the anonymized data. We also demonstrate the effectiveness of our scalable anonymization technique on a real-world location dataset consisting of more than 4 million subscribers of a large European telecom operator. We believe that our technique can be very appealing for practitioners willing to share such large datasets.