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Josep Domingo-Ferrer, Francesc Sebé, Agusti Solanas

Computers & Mathematics with Applications 55 (4), 714-732

Description: Microaggregation is a family of methods for statistical disclosure control (SDC) of microdata (records on individuals and/or companies), that is, for masking microdata so that they can be released without disclosing private information on the underlying individuals. Microaggregation techniques are currently being used by many statistical agencies. The principle of microaggregation is to group original database records into small aggregates prior to publication. Each aggregate should contain at least k records to prevent disclosure of individual information, where k is a constant value preset by the data protector. In addition to it being a good masking method, microaggregation has recently been shown useful to achieve k-anonymity. In k-anonymity, the parameter k specifies the maximum acceptable disclosure risk, so that, once a value for k has been selected, the only job left is to maximize data utility: if microaggregation is used to implement k-anonymity, maximizing utility can be achieved by microaggregating optimally, i.e. with minimum within-groups variability loss. Unfortunately, optimal microaggregation can only be computed in polynomial time for univariate data. For multivariate data, it has been shown to be NP-hard. We present in this paper a polynomial-time approximation to microaggregate multivariate numerical data for which bounds to optimal microaggregation can be derived at least for two different optimality criteria: minimum within-groups Euclidean distance and minimum within-groups sum of squares. Beyond the theoretical interest of being the first microaggregation proposal with proven approximation bounds for any k, our method is empirically shown to be comparable to the best available heuristics for multivariate microaggregation.

Agusti Solanas, Antoni Martinez-Balleste

17th COMPSTAT Symposium of the IASC, Rome, 917-925

Description: Microaggregation is a clustering problem with minimum size constraints on the resulting clusters or groups; the number of groups is unconstrained and the within-group homogeneity should be maximized. In the context of privacy in statistical databases, microaggregation is a well-known approach to obtaining anonymized versions of confidential microdata. Optimally solving microaggregation on multivariate data sets is known to be difficult (NP-hard). Therefore, heuristic methods are used in practice. This paper presents a new heuristic approach to multivariate microaggregation, which provides variable-sized groups (and thus higher within-group homogeneity) with a computational cost similar to the one of fixed-size microaggregation heuristics.

Fran Casino, Josep Domingo-Ferrer, Constantinos Patsakis, Domènec Puig, Agusti Solanas

Journal of Computer and System Sciences 81 (6), 1000-1011

Description: This article proposes a new technique for Privacy Preserving Collaborative Filtering (PPCF) based on microaggregation, which provides accurate recommendations estimated from perturbed data whilst guaranteeing user k-anonymity. The experimental results presented in this article show the effectiveness of the proposed technique in protecting users' privacy without compromising the quality of the recommendations. In this sense, the proposed approach perturbs data in a much more efficient way than other well-known methods such as Gaussian Noise Addition (GNA).

Agusti Solanas, Antoni Martínez-Ballesté

Computer Communications 31 (6), 1181-1191

Description: Location-based services (LBS) will be a keystone of the new information society that is founded on the information and communi- cations technologies (ICTs). Mobile devices such as cell phones or laptops have become ubiquitous. They are equipped with a variety of localisation systems that make them proper for making use of the new LBS. Most of the times, these services are provided by a trusted company (e.g. a telecommunications company). However, the massive use of mobile devices pave the way for the creation of ad hoc wireless networks that can be used to exchange information based on locations. When the exchange of location information is done amongst untrusted parties, the privacy of the participants could be in jeopardy. In this paper we present a novel solution that guarantees the privacy of the users of LBS. Our technique is built up of several modules that progressively increase the privacy level of the users. Unlike the existing approaches, our proposal does not rely on a trusted third party (TTP) to anonymise the users and to guarantee their location privacy.

Agusti Solanas, Josep Domingo-Ferrer, Antoni Martínez-Ballesté

Proceedings of the 1st international workshop on privacy in location-based applications (PILBA), 12-23

Description: Location-Based Services (LBS) are gaining importance due to the advances in mobile networks and positioning technologies. Nevertheless, the wide deployment of LBS can jeopardise the privacy of their users, so ensuring user privacy is paramount to the success of those services. This article surveys the most relevant techniques for guaranteeing location privacy to LBS users. The rigid dichotomy between schemes which rely on Trusted Third Parties (TTP-based) and those which do not (TTP-free) is emphasised. Also, the convenience of both approaches is discussed and some ideas on the future of location privacy in these services are sketched.

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