Saikat Guha, Mudit Jain, and Venkata Padmanabhan
With mobile phones becoming first-class citizens in the online world, the rich location data they bring to the table is set to revolutionize all aspects of online life including content delivery, recommendation systems, and advertising. However, user-tracking is a concern with such location-based services, not only because location data can be linked uniquely to individuals, but because the low-level nature of current location APIs and the resulting dependence on the cloud to synthesize useful representations virtually guarantees such tracking.
In this paper, we propose privacy-preserving location-based matching as a fundamental platform primitive and as an alternative to exposing low-level, latitude-longitude (lat-long) coordinates to applications. Applications set rich location-based triggers and have these be fired based on location updates either from the local device or from a remote device (e.g., a friend’s phone). Our Koi platform, comprising a privacy-preserving matching service in the cloud and a phone-based agent, realizes this primitive across multiple phone and browser platforms. By masking low-level lat-long information from applications, Koi not only avoids leaking privacy-sensitive information, it also eases the task of programmers by providing a higher-level abstraction that is easier for applications to build upon. Koi’s privacy-preserving protocol prevents the cloud service from tracking users. We verify the non-tracking properties of Koi using a theorem prover, illustrate how privacy guarantees can easily be added to a wide range of location-based applications, and show that our public deployment is performant, being able to perform 12K matches per second on a single core.
In Proceedings of the 9th Symposium on Networked Systems Design and Implementation (NSDI)
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