Mobile devices with rich features can record videos, traffic parameters or air quality readings along user trajectories. Although such data may be valuable, users are seldom rewarded for collecting them. Emerging digital marketplaces allow owners to advertise their data to interested buyers. We focus on geo-marketplaces, where buyers search data based on geo-tags. Such marketplaces present significant challenges. First, if owners upload data with revealed geo-tags, they expose themselves to serious privacy risks. Second, owners must be accountable for advertised data, and must not be allowed to subsequently alter geo-tags. Third, such a system may be vulnerable to intensive spam activities, where dishonest owners flood the system with fake advertisements. We propose a geo-marketplace that addresses all these concerns. We employ searchable encryption, digital commitments, and blockchain to protect the location privacy of owners while at the same time incorporating accountability and spam-resilience mechanisms. We implement a prototype with two alternative designs that obtain distinct trade-offs between trust assumptions and performance. Our experiments on real location data show that one can achieve the above design goals with practical performance and reasonable financial overhead. CCS CONCEPTS• Security and privacy → Domain-specific security and privacy architectures.

Location entropy (LE) is a popular metric for measuring the popularity of various locations (e.g., points-of-interest). Unlike other metrics computed from only the number of (unique) visits to a location, namely frequency, LE also captures the diversity of the users' visits, and is thus more accurate than other metrics. Current solutions for computing LE require full access to the past visits of users to locations, which poses privacy threats. This paper discusses, for the first time, the problem of perturbing location entropy for a set of locations according to differential privacy. The problem is challenging because removing a single user from the dataset will impact multiple records of the database; i.e., all the visits made by that user to various locations. Towards this end, we first derive non-trivial, tight bounds for both local and global sensitivity of LE, and show that to satisfy -differential privacy, a large amount of noise must be introduced, rendering the published results useless. Hence, we propose a thresholding technique to limit the number of users' visits, which significantly reduces the perturbation error but introduces an approximation error. To achieve better utility, we extend the technique by adopting two weaker notions of privacy: smooth sensitivity (slightly weaker) and crowd-blending (strictly weaker). Extensive experiments on synthetic and real-world datasets show that our proposed techniques preserve original data distribution without compromising location privacy.

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