Abstract:The continuously increasing use of locationbased services poses an important threat to the privacy of users. A natural defense is to employ an obfuscation mechanism, such as those providing geoindistinguishability, a framework for obtaining formal privacy guarantees that has become popular in recent years. Ideally, one would like to employ an optimal obfuscation mechanism, providing the best utility among those satisfying the required privacy level. In theory optimal mechanisms can be constructed via linear programming. In practice, however, this is only feasible for a radically small number of locations. As a consequence, all known applications of geo-indistinguishability simply use noise drawn from a planar Laplace distribution. In this work, we study methods for substantially improving the utility of location obfuscation, while maintaining practical applicability as a main goal. We provide such solutions for both infinite (continuous or discrete) as well as large but finite domains of locations, using a Bayesian remapping procedure as a key ingredient. We evaluate our techniques in two real world complete datasets, without any restriction on the evaluation area, and show important utility improvements with respect to the standard planar Laplace approach.

Abstract. Probabilistic trust has been adopted as an approach to taking security sensitive decisions in modern global computing environments. Existing probabilistic trust frameworks either assume fixed behaviour for the principals or incorporate the notion of 'decay' as an ad hoc approach to cope with their dynamic behaviour. Using Hidden Markov Models (HMMs) for both modelling and approximating the behaviours of principals, we introduce the HMM-based trust model as a new approach to evaluating trust in systems exhibiting dynamic behaviour. This model avoids the fixed behaviour assumption which is considered the major limitation of existing Beta trust model. We show the consistency of the HMM-based trust model and contrast it against the well known Beta trust model with the decay principle in terms of the estimation precision.

The growing popularity of Location-Based Services, allowing for the collection of huge amounts of information regarding users' locations, has started raising serious privacy concerns. In this survey we analyze the various kinds of privacy breaches that may arise in connection with the use of location-based services, and we consider and compare some of the mechanisms and the metrics that have been proposed to protect the user's privacy, focusing in particular on the comparison between probabilistic spatial obfuscation techniques.

a b s t r a c tResearch in models for experience-based trust management has either ignored the problem of modelling and reasoning about dynamically changing principal behaviour, or provided ad hoc solutions to it. Probability theory provides a foundation for addressing this and many other issues in a rigorous and mathematically sound manner. Using Hidden Markov Models to represent principal behaviours, we focus on computational trust frameworks based on the 'beta' probability distribution and the principle of exponential decay, and derive a precise analytical formula for the estimation error they induce. This allows potential adopters of beta-based computational trust frameworks and algorithms to better understand the implications of their choice.

No abstract