Server-Aided Privacy-Preserving Proximity Testing

Proximity testing is at the core of many Location-Based online Services (LBS) which we use in our daily lives to order taxis, find places of interest nearby, connect with people. Currently, most such services expect a user to submit his location to them and trust the LBS not to abuse this information, and use it only to provide the service. Existing cases of such information being misused (e.g., by the LBS employees or criminals who breached its security) motivates the search for better solutions that would ensure the privacy of user data, and give users control of how their data is being used.In this thesis, we address this problem using cryptographic techniques. We propose three cryptographic protocols that allow two users to perform proximity testing (check if they are close enough to each other) with the help of two servers.In the papers 1 and 2, the servers are introduced in order to allow users not to be online at the same time: one user may submit their location to the servers and go offline, the other user coming online later and finishing proximity testing. The drastically improves the practicality of such protocols, since the mobile devices that users usually run may not always be online. We stress that the servers in these protocols merely aid the users in performing the proximity testing, and none of the servers can independently extract the user data.In the paper 3, we use the servers to offload the users\u27 computation and communication to. The servers here pre-generate correlated random data and send it to users, who can use it to perform a secure proximity testing protocol faster. Paper 3, together with the paper 2, are highly practical: they provide strong security guarantees and are suitable to be executed on resource-constrained mobile devices. In fact, the work of clients in these protocols is close to negligible as most of the work is done by servers

Investigating the Privacy vs. Forwarding Accuracy Tradeoff in Opportunistic Interest-Casting

Many mobile social networking applications are based on a ``friend proximity detection" step, according to which two mobile users try to jointly estimate whether they have friends in common, or share similar interests, etc. Performing ``friend proximity detection" in a privacy-preserving way is fundamental to achieve widespread acceptance of mobile social networking applications. However, the need of privacy preservation is often at odds with application-level performance of the mobile social networking application, since only obfuscated information about the other user\u27s profile is available for optimizing performance. noindent In this paper, we study for the first time the fundamental tradeoff between privacy preservation and application-level performance in mobile social networks. More specifically, we consider a mobile social networking application for opportunistic networks called interest-casting. In the interest-casting model, a user wants to deliver a piece of information to other users sharing similar interests (``friends"), possibly through multi-hop forwarding. In this paper, we propose a privacy-preserving friend proximity detection scheme based on a protocol for solving the Yao\u27s ``Millionaire\u27s Problem", and we introduce three interest-casting protocols achieving different tradeoffs between privacy and accuracy of the information forwarding process. The privacy vs. accuracy tradeoff is analyzed both theoretically, and through simulations based on a real-world mobility trace. The results of our study demonstrate for the first time that privacy preservation is at odds with forwarding accuracy, and that the best tradeoff between these two conflicting goals should be identified based on the application-level requirements

Security and Privacy in Online Social Networks

The explosive growth of Online Social Networks (OSNs) over the past few years has redefined the way people interact with existing friends and especially make new friends. OSNs have also become a great new marketplace for trade among the users. However, the associated privacy risks make users vulnerable to severe privacy threats. In this dissertation, we design protocols for private distributed social proximity matching and a private distributed auction based marketplace framework for OSNs. In particular, an OSN user looks for matching profile attributes when trying to broaden his/her social circle. However, revealing private attributes is a potential privacy threat. Distributed private profile matching in OSNs mainly involves using cryptographic tools to compute profile attributes matching privately such that no participating user knows more than the common profile attributes. In this work, we define a new asymmetric distributed social proximity measure between two users in an OSN by taking into account the weighted profile attributes (communities) of the users and that of their friends’. For users with different privacy requirements, we design three private proximity matching protocols with increasing privacy levels. Our protocol with highest privacy level ensures that each user’s proximity threshold is satisfied before revealing any matching information. The use of e-commerce has exploded in the last decade along with the associated security and privacy risks. Frequent security breaches in the e-commerce service providers’ centralized servers compromise consumers’ sensitive private and financial information. Besides, a consumer’s purchase history stored in those servers can be used to reconstruct the consumer’s profile and for a variety of other privacy intrusive purposes like directed marketing. To this end, we propose a secure and private distributed auction framework called SPA, based on decentralized online social networks (DOSNs) for the first time in the literature. The participants in SPA require no trust among each other, trade anonymously, and the security and privacy of the auction is guaranteed. The efficiency, in terms of communication and computation, of proposed private auction protocol is at least an order of magnitude better than existing distributed private auction protocols and is suitable for marketplace with large number of participants