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User-private information retrieval systems should protect the user's anonymity when performing queries against a database, or they should limit the servers capacity of profiling users. Peer-to-peer user-private information retrieval (P2P UPIR) supplies a practical solution: the users in a group help each other in doing their queries, thereby preserving(More)
In this article we provide a formal framework for reidentification in general. We define n-confusion as a concept for modelling the anonymity of a database table and we prove that n-confusion is a generalization of k-anonymity. After a short survey on the different available definitions of k-anonymity for graphs we provide a new definition for k-anonymous(More)
User-private information retrieval (UPIR) is the art of retrieving information without telling the information holder who you are. UPIR is sometimes called anonymous keyword search. This article discusses a UPIR protocol in which the users form a peer-to-peer network over which they collaborate in protecting the privacy of each other. The protocol is known(More)
—In this paper we discuss some tools for graph perturbation with applications to data privacy. We present and analyse two different approaches. One is based on matrix decomposition and the other on graph partitioning. We discuss these methods and show that they belong to two traditions in data protection: noise addition/microaggregation and k-anonymity. I.(More)
We provide a formal framework for re-identification in general. We define <i>n</i>-confusion as a concept for modelling the anonymity of a database table and we prove that <i>n</i>-confusion is a generalization of <i>k</i>-anonymity. Finally we present an example to illustrate how this result can be used to augment local variance in <i>k</i>-anonymous(More)
A long list of personal tragedies, including teenage suicides, has raised the importance of managing the personal information available on the Internet. It has been argued that it should be allowed to make mistakes, and that there should be a right to be forgotten. Unfortunately, today's Internet architecture and services typically do not support such(More)
Re-identification algorithms are used in data privacy to measure disclosure risk. They model the situation in which an adversary attacks a published database by means of linking the information of this adversary with the database. In this paper we formalize this type of algorithm in terms of true probabilities and compatible belief functions. The purpose of(More)