Privacy-preserving Messaging and Search: A Collaborative Approach

Privacy-preserving Messaging and Search: A Collaborative ApproachbyGiulia Cecilia FantiDoctor of Philosophy in Engineering – Electrical Engineering and Computer SciencesUniversity of California, BerkeleyProfessor Kannan Ramchandran, Chair In a free society, people have the right to consume and share public data without fear of retribu-tion. However, today’s technological landscape enables large-scale monitoring and censorship ofnetworks by powerful entities (e.g., totalitarian governments); at worst, these entities may punishpeople for the information they consume or the opinions they espouse. This thesis considers twoproblems aimed at empowering people to freely share and consume public information: anony-mous message spreading and privacy-preserving database search. In both areas, we present algo-rithmic innovations and analyze their correctness and efficiency. The key assumption underlyingour work is that centralized architectures cannot reliably provide privacy-preserving services; notonly are the incentive structures misaligned, but centralized infrastructures are often vulnerable toexternal breaches by hackers or government agencies, for example. We therefore restrict ourselvesto distributed algorithms that rely on cooperation and resource-sharing between privacy-consciousindividuals.In the area of anonymous message spreading, we consider a user who wishes to spread amessage to as many people as possible over an underlying connectivity network (e.g., a socialnetwork); this is the premise of Yik Yak, Whisper, and other popular anonymous messaging net-works. Most existing social networks (anonymous or not) use a push-based mechanism to spreadcontent to all of a user’s neighbors on the contact network; if a neighbor approves the content by‘liking’ it, this symmetric spreading propagates to the neighbor’s neighbors, and so forth. Recentresearch suggests that under this spreading model, the true author of a message can be identifiedwith non-negligible probability by a powerful global adversary. We propose an alternative, dis-tributed spreading mechanism called adaptive diffusion, which breaks this symmetry. We showtheoretically that adaptive diffusion gives optimal or asymptotically-optimal anonymity guaran-tees over certain classes of synthetic graphs for various adversarial models, while spreading nearlyas fast as traditional symmetric mechanisms. On real-world graphs, we demonstrate empiricallythat adaptive diffusion gives significantly stronger anonymity properties than existing spreadingmechanisms.In the area of privacy-preserving search, we consider the foundations of a distributed, privacy-preserving search engine built over public data. Architecturally, we envision a peer-to-peer (P2P)