Modern network security research has demonstrated a clear need for open sharing of traffic datasets between organizations, a need that has so far been superseded by the challenge of removing sensitive content beforehand. Network Data Anonymization (NDA) is emerging as a field dedicated to this problem, with its main direction focusing on removal of identifiable artifacts that might pierce priva...

We consider the privacy problem in data publishing: given a database instance containing sensitive information “anonymize” it to obtain a view such that, on one hand attackers cannot learn any sensitive information from the view, and on the other hand legitimate users can use it to compute useful statistics. These are conflicting goals. In this paper we prove an almost crisp separation of the c...

In this work, we focus on protection against identity disclosure in the publication of sparse multidimensional data. Existing multidimensional anonymization techniques (a) protect the privacy of users either by altering the set of quasi-identifiers of the original data (e.g., by generalization or suppression) or by adding noise (e.g., using differential privacy) and/or (b) assume a clear distin...

The publication of network traces is critical for network research but their release is highly constrained by privacy and security concerns. The importance of a framework for anonymizing traces to provide different levels of security and utility to promote trace publication has been identified in the literature. However, the current state-of-art anonymization techniques have failed to provide t...

Location based services (LBS) aim to deliver information based on a mobile user’s location. However, knowledge of the location can be used by an adversary to physically locate the person, leading to the risk of physical harm, as well as possible leakage of certain personal information. This has serious consequences on privacy. The concept of location kanonymity has been proposed to address this...

How to protect individual privacy in public data is always a concern. For social networks, the challenge is that, the structure of the social network graph can be utilized to infer the private and sensitive information of users. The existing anonymity schemes mostly focus on the anonymity of vertex identities, such that a malicious attacker cannot associate an user with a specific vertex. In re...

Adversary: Somebody who, whether intentionally or not, reveals sensitive, private information Adversarial model: Formal description of the unique characteristics of a particular adversary Attribute disclosure: A privacy breach wherein some descriptive attribute of somebody is revealed Identity disclosure: A privacy breach in which a presumably anonymous person is in fact identifiable k-P-anonym...

Recent work on anonymizing online social networks (OSNs) has looked at privacy preserving techniques for publishing a single instance of the network. However, OSNs evolve and a single instance is inadequate for analyzing their evolution or performing longitudinal data analysis. We study the problem of repeatedly publishing OSN data as the network evolves while preserving privacy of users. Publi...

Privacy is amongst the major concerns when publishing or sharing social network data for social science research and also business analysis. Recently, researchers have developed privacy models just like k-anonymity in order to avoid node reidentification through structure information. However, even though these privacy models are enforced, an attacker can always have the capacity to infer one’s...

Anonymization technique has been extensively studied and widely applied for privacy-preserving data publishing. In most previous approaches, a microdata table consists of three categories attribute: explicit-identifier, quasi-identifier (QI), sensitive attribute. Actually, different individuals may have view on the sensitivity attributes. Therefore, there is another type attribute that contains...