When issuing a one-shot or continuous content-based subscription, there is an inherent tradeoff between the privacy of the subscriber and the accuracy of the matching notifications. The former can be described in terms of how well the exposed information uniquely characterized the subscriber, and the latter how well the returned data items match the subscriber’s real interests. In this paper, w...

Recently, several anonymization algorithms have appeared for privacy preservation on graphs. Some of them are based on randomization techniques and on k-anonymity concepts. We can use both of them to obtain an anonymized graph with a given k-anonymity value. In this paper we compare algorithms based on both techniques in order to obtain an anonymized graph with a desired k-anonymity value. We w...

Graph anonymization aims at reducing the ability of an attacker to identify nodes a graph by obfuscating its structural information. In k-anonymity, this means making each node indistinguishable from least other k-1 nodes. Simply stripping their identifying label is insufficient, as with enough knowledge can still recover identities. We propose algorithm enforce k-anonymity based on Szemerédi r...

Privacy preservation is an important issue in the release of data for mining purposes. The k-anonymity model has been introduced for protecting individual identification. Recent studies show that a more sophisticated model is necessary to protect the association of individuals to sensitive information. In this paper, we propose an (α, k)-anonymity model to protect both identifications and relat...

This paper explains why existing anonymity models such as k-anonymity cannot be applied to the most common form of private data release on the internet, social network APIs. An alternative anonymity model, PP-anonymity, is presented, which measures the posterior probability of an attacker logically deducing previously unknown private information using a social network API. Finally, the feasibil...

The usual approach to generate k-anonymous data sets, based on generalization of the quasi-identifier attributes, does not provide any control on the variability of the confidential attributes within the k-anonymous groups. If the latter variability is too small, privacy is not sufficiently protected, while, for large variabilities, data utility is substantially damaged. Some refinements to the...