We present PRIVACYGRID − a framework for supporting anonymous location-based queries in mobile information delivery systems. The PRIVACYGRID framework offers three unique capabilities. First, we provide a location privacy preference profile model, called location P3P, which allows mobile users to explicitly define their preferred location privacy requirements in terms of both location hiding me...

A well-known privacy-preserving network data publication problem focuses on how to publish social network data while protecting privacy and permitting useful analysis. Designing algorithms that safely transform network data is an active area of research. The process of applying these transformations is called anonymization operation. The authors recently proposed the (α,β,γ,δ)-SNP (Social Netwo...

We investigate the privacy and utility aspects of k-anonymity, which has received much research attention since its introduction in [Sweeney, 2002]. Meyerson and Williams [2004] showed that finding an optimal k-anonymization is NP-hard and developed a first approximation algorithm. Further algorithms with different approximation guarantees have been proposed, but it remains hard to compare thes...

Translational medical research is an emerging concept that aims at transforming discoveries from basic sciences into diagnostic and therapeutic applications. In the opposite direction, clinical data are needed for feedback and as stimuli for the generation of new research hypotheses. This process is highly data-intensive and centered around the idea of integrating data from basic biomedical sci...

Knowledge graphs (KGs) play an essential role in data sharing because they can model both users’ attributes and their relationships. KGs tailor many analyses, such as classification where a sensitive attribute is selected the analyst analyzes associations between users attribute’s values (aka values). Data providers anonymize share anonymized versions to protect privacy. Unfortunately, adversar...

Data sharing is a valuable tool for improving security. It allows integrating information from multiple sources to better identify and respond to global security threats. On the other side, sharing of data is limited by privacy and confidentiality. A possible solution is removing or obfuscating part of the data before release (anonymization), and, to this scope, various masking algorithms have ...

The publishing of data with privacy guarantees is a task typically performed by a data curator who is expected to provide guarantees for the data he publishes in quantitative fashion, via a privacy criterion (e.g., k-anonymity, l-diversity). The anonymization of data is typically performed off-line. In this paper, we provide algorithmic tools that facilitate the negotiation for the anonymizatio...

The sharing of network traces is an important prerequisite for the development and evaluation of efficient anomaly detection mechanisms. Unfortunately, privacy concerns and data protection laws prevent network operators from sharing these data. Anonymization is a promising solution in this context; however, it is unclear if the sanitization of data preserves the traffic characteristics or intro...

Collecting network traffic traces from deployed networks is one of the basic steps in network research. These traces can be used to study real users, traffic engineering, packet classification, web performance, security application or more general network measurement and simulation. However for security and privacy reason monitored traffic traces have to be modified before they are published. T...