Participatory sensing is emerging as an innovative computing paradigm that targets the ubiquity of always-connected mobile phones and their sensing capabilities. In this context, a multitude of pioneering applications increasingly carry out pervasive collection and dissemination of information and environmental data, such as, traffic conditions, pollution, temperature, etc. Participants collect and report measurements from their mobile devices and entrust them to the cloud to be made available to applications and users. Naturally, due to the personal information associated to the reports (e.g., location, movements, etc.), a number of privacy concerns need to be taken into account prior to a large-scale deployment of these applications. Motivated by the need for privacy protection in Participatory Sensing, this work presents PEPSI: a Privacy-Enhanced Participatory Sensing Infrastructure. We explore realistic architectural assumptions and a minimal set of formal requirements aiming at protecting privacy of both data producers and consumers. We propose two instantiations that attain privacy guarantees with provable security at very low additional computational cost and almost no extra communication overhead.
No provable privacy. User privacy in previous work (e.g., @cite_17 @cite_40 ) relies on Mix Networks @cite_31 , an anonymizing technique used to de-link submitted reports from their origin, before they reach applications. In other words, a Mix Network acts as an anonymizing proxy and forwards user reports only when the set of received reports satisfies a system-defined criteria. Privacy metrics such as @math -anonymity @cite_16 or @math -diversity @cite_26 have been defined to characterize privacy through Mix Networks. For example, a Mix Network that provides @math -anonymity batches'' reports so that it is not possible to link a given report to its sender, among a set of @math reports. Clearly, anonymity is not guaranteed but it rather depends on the number of reports received and mixed'' by the Mix Network. Moreover, there could be scenarios where a relatively long time could pass before the desired level of anonymity is reached (when enough'' reports have been collected).
@cite_26 @cite_40 @cite_31 @cite_16 @cite_17
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An improvement over the previously known disclosure attack is presented that allows, using statistical methods, to effectively deanonymize users of a mix system. Furthermore the statistical disclosure attack is computationally efficient, and the conditions for it to be possible and accurate are much better understood. The new attack can be generalized easily to a variety of anonymity systems beyond mix networks. A user is only anonymous within a set of other users. Hence, the core functionality of an anonymity providing technique is to establish an anonymity set. In open environments, such as the Internet, the established anonymity sets in the whole are observable and change with every anonymous communication. We use this fact of changing anonymity sets and present a model where we can determine the protection limit of an anonymity technique, i.e. the number of observations required for an attacker to "break" uniquely a given anonymity technique. In this paper, we use the popular MIX method to demonstrate our attack. The MIX method forms the basis of most of the today's deployments of anonymity services (e.g. Freedom, Onion Routing, Webmix). We note that our approach is general and can be applied equally well to other anonymity providing techiques. There are different methods to build an anonymity service using MIXes. A substantial decision for doing so is the method of choosing the MIX route. In this paper we compare two special configurations: a fixed MIX route used by all participants and a network of freely usable MIXes where each participant chooses his own route. The advantages and disadvantages in respect to the freedom of choice are presented and examined. We'll show that some additional attacks are possible in networks with freely chosen MIX routes. After describing these attacks, we estimate their impact on the achievable degree of anonymity. Finally, we evaluate the relevance of the described attacks with respect to existing systems like e.g. Mixmaster, Crowds, and Freedom. It is not uncommon in the data anonymization literature to oppose the "old" @math k -anonymity model to the "new" differential privacy model, which offers more robust privacy guarantees. Yet, it is often disregarded that the utility of the anonymized results provided by differential privacy is quite limited, due to the amount of noise that needs to be added to the output, or because utility can only be guaranteed for a restricted type of queries. This is in contrast with @math k -anonymity mechanisms, which make no assumptions on the uses of anonymized data while focusing on preserving data utility from a general perspective. In this paper, we show that a synergy between differential privacy and @math k -anonymity can be found: @math k -anonymity can help improving the utility of differentially private responses to arbitrary queries. We devote special attention to the utility improvement of differentially private published data sets. Specifically, we show that the amount of noise required to fulfill @math ? -differential privacy can be reduced if noise is added to a @math k -anonymous version of the data set, where @math k -anonymity is reached through a specially designed microaggregation of all attributes. As a result of noise reduction, the general analytical utility of the anonymized output is increased. The theoretical benefits of our proposal are illustrated in a practical setting with an empirical evaluation on three data sets.