Abstract
Complex networks gathered from our online interactions provide a rich source of information that can be used to try to model and predict our behavior. While this has very tangible benefits that we have all grown accustomed to, there is a concrete privacy risk in sharing potentially sensitive data about ourselves and the people we interact with, especially when this data is publicly available online and unprotected from malicious attacks. k-anonymity is a technique aimed at reducing this risk by obfuscating the topological information of a graph that can be used to infer the nodes' identity. In this paper we propose a novel algorithm to enforce k-anonymity based on a well-known result in extremal graph theory, the Szemerédi regularity lemma. Given a graph, we start by computing a regular partition of its nodes. The Szemerédi regularity lemma ensures that such a partition exists and that the edges between the sets of nodes behave almost randomly. With this partition, we anonymize the graph by randomizing the edges within each set, obtaining a graph that is structurally similar to the original one yet the nodes within each set are structurally indistinguishable. We test the proposed approach on real-world networks extracted from Facebook. Our experimental results show that the proposed approach is able to anonymize a graph while retaining most of its structural information.
Highlights
The beginning of the twenty-first century has been characterized by the rise of online social media and data-hungry artificial intelligence (AI)
While most of the previous k-anonymity approaches assume that the attacker has access only to a certain level of structural information, in this paper we propose a method that creates k-anonymous groups of nodes where no degree of structural information can help to break the anonymity guarantee
In the previous section we introduced the Szemerédi regularity lemma and we showed how this can be seen as a first step toward obtaining a k-anonymous graph
Summary
The beginning of the twenty-first century has been characterized by the rise of online social media and data-hungry artificial intelligence (AI) In this context, sophisticated machine learning algorithms feed off massive amounts of data produced by our digital personas to perfect the way they model and predict our behavior, both online and offline. One common example is that of a user for which the number of connections in the network is known (i.e., the number of friends on Facebook) and this number happens to be unique for that individual. In other words, this piece of information alone would be sufficient to identify that user among the rest of the nodes. The individual may turn out to belong to a group of nodes labeled with a certain sensitive attribute, e.g., health condition
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