Privacy-preserving distance computation and proximity testing on earth, done right

Abstract

In recent years, the availability of GPS-enabled smartphones have made location-based services extremely popular. A multitude of applications rely on location information to provide a wide range of services. Location information is, however, extremely sensitive and can be easily abused. In this paper, we introduce the first protocols for secure computation of distance and for proximity testing over a sphere. Our secure distance protocols allow two parties, Alice and Bob, to determine their mutual distance without disclosing any additional information about their location. Through our secure proximity testing protocols, Alice only learns if Bob is in close proximity, i.e., within some arbitrary distance.Our techniques rely on three different representations of Earth, which provide different trade-offs between accuracy and performance. We show, via experiments on a prototype implementation, that our protocols are practical on resourceconstrained smartphone devices. Our distance computation protocols runs, in fact, in 54 to 78 ms on a commodity Android smartphone. Similarly, our proximity tests require between 1.2 s and 2.8 s on the same platform. The imprecision introduced by our protocols is very small, i.e., between 0.1% and 3% on average, depending on the distance.

InnerCircle: A parallelizable decentralized privacy-preserving location proximity protocol

“…We discuss the most prominent approaches in relation to InnerCircle. Seděnka and Gasti [29] homomorphically compute distances using the UTM projection, ECEF (Earth-Centered Earth-Fixed) coordinates, and using the Haversine formula. Haversine and ECEF are both useful when considering the curvature of the earth.…”

Faster Privacy-Preserving Location Proximity Schemes

“…So far several solutions for privacy-preserving location proximity (PPLP) schemes have been proposed, e.g., [6,16,25,[29][30][31][35][36][37]. In early literature [6], privacy-preserving location proximity computation is realized by an imprecise location-based range query that allows a user to approximately learn if any of its communication partners is within a fixed distance from her current location.…”

“…The proximity detection is achieved by testing whether two users share at least one triangle. However, as discussed in [29], the protocols of [25] may introduce different errors in practice.…”

Circular Range Search on Encrypted Spatial Data

“…Some recent works [2], [25], [26] considering private proximity testing directly between two users are also related to circular range search over encrypted spatial data we study in this paper. Specifically, with private proximity testing, a user Alice is able to test whether another user Bob is within some certain distance without revealing any user's specific location to each other.…”

“…In these schemes [2], [25], [26], the private computation and comparison of the distance between Alice and Bob are generally achieved by using Secure Two-Party Computation [27] (e.g., Garbled Circuits [26]), which inevitably introduce multiple rounds of interactions between the two parties. While our work is targeting a design with a minimal one-round clientserver interaction (see some further discussions in the next section).…”