Decentralized online social networks (OSNs) typically rely on the existence of a public key infrastructure (PKI), but certificate authorities (CAs) cannot scalably identify all of the members of an OSN. Our system, LoKI, uses the ubiquity of mobile devices to exchange secrets during real-world meetings that can be used for the purposes of identification in-band, allowing each user to easily discover the keys of many one-hop relationships in the OSN.

Current IP geoloation techniques can geolocate an IP address to a region approximately 700 square miles, roughly the size of a metropolitan area. We model geolocation as a pattern-recognition problem, and introduce techniques that geolocate addresses to within 5 miles inside a metropolitan area. We propose two complementary algorithms: The first algorithm, Pattern Based Geolocation (PBG), models the distribution of latencies to the target and compares it to those of the reference landmarks to resolve an address to within 5 miles in a metropolitan area. The second approach, Perturbation Augmented PBG (PAPBG), provides higher resolution by sending extra traffic in the network. While sending an aggregate of 600 Kbps extra traffic to 20 nodes for approximately 2 minutes, PAPBG geolocates addresses to within 3 miles.

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Online social networks (OSNs) are immensely popular, with some claiming over 200 million users. Users share private content, such as personal information or photographs, using OSN applications. Users must trust the OSN service to protect personal information even as the OSN provider benefits from examining and sharing that information. We present Persona, an OSN where users dictate who may access their information. Persona hides user data with attribute-based encryption (ABE), allowing users to apply fine-grained policies over who may view their data. Persona provides an effective means of creating applications in which users, not the OSN, define policy over access to private data. We demonstrate new cryptographic mechanisms that enhance the general applicability of ABE. We show how Persona provides the functionality of existing online social networks with additional privacy benefits. We describe an implementation of Persona that replicates Facebook applications and show that Persona provides acceptable performance when browsing privacy-enhanced web pages, even on mobile devices.