Faster Privacy-Preserving Location Proximity Schemes

Peer reviewe

Longitude : a privacy-preserving location sharing protocol for mobile applications

Location sharing services are becoming increasingly popular. Although many location sharing services allow users to set up privacy policies to control who can access their location, the use made by service providers remains a source of concern. Ideally, location sharing providers and middleware should not be able to access users’ location data without their consent. In this paper, we propose a new location sharing protocol called Longitude that eases privacy concerns by making it possible to share a user’s location data blindly and allowing the user to control who can access her location, when and to what degree of precision. The underlying cryptographic algorithms are designed for GPS-enabled mobile phones. We describe and evaluate our implementation for the Nexus One Android mobile phone

Privacy preserving in indoor fingerprint localization and radio map expansion

People spend most of their life time in indoor environments and in all of these environments, Location Service Providers (LSPs) improve users’ navigation. Preserving privacy in Location Based Services (LBSs) is vital for indoor LBSs and fingerprinting based indoor localization method is an emerging technique in indoor localization. In such systems, LSP may be curious and untrusted. Therefore, it is preferred that user estimates its location by using a Partial Radio Map (PRM) which is achieved by LSP, anonymously. In this paper, a privacy preserving method that uses Bloom filter for preserving anonymity and creating PRM during localization process, is proposed. In this method, LSP cannot recognize user identity, which is anonymized by the anonymizer. The proposed method has lower computational complexity compared with methods that use encryption or clustering concepts. The proposed method also has higher accuracy in localization compared with those that use Bloom filter with one random selected AP. Then, in order to decrease the complexity and to increase the accuracy at the same time, we introduce a method that expands the radio map by authenticated users, without compromising their privacy. We also enhance the performance of this method, using Hilbert curve for preserving the ambiguity of users’ location. After verifying the user’s data, LSP sends a certificate to the authenticated users. This certificate can increase the priority of users in LBS requests. Simulation results and measurements show that the proposed method on average improves the localization accuracy up to 16% compared with existing location privacy methods

Privately Connecting Mobility to Infectious Diseases via Applied Cryptography

Human mobility is undisputedly one of the critical factors in infectious disease dynamics. Until a few years ago, researchers had to rely on static data to model human mobility, which was then combined with a transmission model of a particular disease resulting in an epidemiological model. Recent works have consistently been showing that substituting the static mobility data with mobile phone data leads to significantly more accurate models. While prior studies have exclusively relied on a mobile network operator's subscribers' aggregated data, it may be preferable to contemplate aggregated mobility data of infected individuals only. Clearly, naively linking mobile phone data with infected individuals would massively intrude privacy. This research aims to develop a solution that reports the aggregated mobile phone location data of infected individuals while still maintaining compliance with privacy expectations. To achieve privacy, we use homomorphic encryption, zero-knowledge proof techniques, and differential privacy. Our protocol's open-source implementation can process eight million subscribers in one and a half hours. Additionally, we provide a legal analysis of our solution with regards to the EU General Data Protection Regulation.Comment: Added differentlial privacy experiments and new benchmark

Socially-Aware Distributed Hash Tables for Decentralized Online Social Networks

Many decentralized online social networks (DOSNs) have been proposed due to an increase in awareness related to privacy and scalability issues in centralized social networks. Such decentralized networks transfer processing and storage functionalities from the service providers towards the end users. DOSNs require individualistic implementation for services, (i.e., search, information dissemination, storage, and publish/subscribe). However, many of these services mostly perform social queries, where OSN users are interested in accessing information of their friends. In our work, we design a socially-aware distributed hash table (DHTs) for efficient implementation of DOSNs. In particular, we propose a gossip-based algorithm to place users in a DHT, while maximizing the social awareness among them. Through a set of experiments, we show that our approach reduces the lookup latency by almost 30% and improves the reliability of the communication by nearly 10% via trusted contacts.Comment: 10 pages, p2p 2015 conferenc