Toward practical and private online services

Today's common online services (social networks, media streaming, messaging, email, etc.) bring convenience. However, these services are susceptible to privacy leaks. Certainly, email snooping by rogue employees, email server hacks, and accidental disclosures of user ratings for movies are some sources of private information leakage. This dissertation investigates the following question: Can we build systems that (a) provide strong privacy guarantees to the users, (b) are consistent with existing commercial and policy regimes, and (c) are affordable? Satisfying all three requirements simultaneously is challenging, as providing strong privacy guarantees usually necessitates either sacrificing functionality, incurring high resource costs, or both. Indeed, there are powerful cryptographic protocols---private information retrieval (PIR), and secure two-party computation (2PC)---that provide strong guarantees but are orders of magnitude more expensive than their non-private counterparts. This dissertation takes these protocols as a starting point and then substantially reduces their costs by tailoring them using application-specific properties. It presents two systems, Popcorn and Pretzel, built on this design ethos. Popcorn is a Netflix-like media delivery system, that provably hides, even from the content distributor (for example, Netflix), which movie a user is watching. Popcorn tailors PIR protocols to the media domain. It amortizes the server-side overhead of PIR by batching requests from the large number of concurrent users retrieving content at any given time; and, it forms large batches without introducing playback delays by leveraging the properties of media streaming. Popcorn is consistent with the prevailing commercial regime (copyrights, etc.), and its per-request dollar cost is 3.87 times that of a non-private system. The other system described in this dissertation, Pretzel, is an email system that encrypts emails end-to-end between senders and intended recipients, but allows the email service provider to perform content-based spam filtering and targeted advertising. Pretzel refines a 2PC protocol. It reduces the resource consumption of the protocol by replacing the underlying encryption scheme with a more efficient one, applying a packing technique to conserve invocations of the encryption algorithm, and pruning the inputs to the protocol. Pretzel's costs, versus a legacy non-private implementation, are estimated to be up to 5.4 times for the email provider, with additional but modest client-side requirements. Popcorn and Pretzel have fundamental connections. For instance, the cryptographic protocols in both systems securely compute vector-matrix products. However, we observe that differences in the vector and matrix dimensions lead to different system designs. Ultimately, both systems represent a potentially appealing compromise: sacrifice some functionality to build in strong privacy properties at affordable costs.Computer Science

Proving the correct execution of concurrent services in zero-knowledge

This paper introduces Spice, a system for building verifiable state machines (VSMs). A VSM is a request-processing service that produces proofs establishing that requests were executed correctly according to a specification. Such proofs are succinct (a verifier can check them efficiently without reexecution) and zero-knowledge (a verifier learns nothing about the content of the requests, responses, or the internal state of the service). Recent systems for proving the correct execution of stateful computations---Pantry, Geppetto, CTV, vSQL, etc.--implicitly implement VSMs, but they incur prohibitive costs. Spice reduces these costs significantly with a new storage primitive. More notably, Spice’s storage primitive supports multiple writers, making Spice the first system that can succinctly prove the correct execution of concurrent services. We find that Spice running on a cluster of 16 servers achieves 488--1167 transactions/second for a variety of applications including inter-bank transactions, cloud-hosted ledgers, and dark pools. This represents an 18,000--685,000× higher throughput than prior work

Coeus: A System for Oblivious Document Ranking and Retrieval

Given a private string q and a remote server that holds a set of public documents D, how can one of the K most relevant documents to q in D be selected and viewed without anyone (not even the server) learning anything about q or the document? This is the oblivious document ranking and retrieval problem. In this paper, we describe Coeus, a system that solves this problem. At a high level, Coeus composes two cryptographic primitives: secure matrix-vector product for scoring document relevance using the widely-used term frequency-inverse document frequency (tf-idf) method, and private information retrieval (PIR) for obliviously retrieving documents. However, Coeus reduces the time to run these protocols, thereby improving the user-perceived latency, which is a key performance metric. Coeus first reduces the PIR overhead by separating out private metadata retrieval from document retrieval, and it then scales secure matrix-vector product to tf-idf matrices with several hundred billion elements through a series of novel cryptographic refinements. For a corpus of English Wikipedia containing 5 million documents, a keyword dictionary with 64K keywords, and on a cluster of 143 machines on AWS, Coeus enables a user to obliviously rank and retrieve a document in 3.9 seconds---a 24x improvement over a baseline system

PrivaTube : Privacy-Preserving Edge-Assisted Video Streaming

Video on Demand (VoD) streaming is the largest source of Internet traffic. Efficient and scalable VoD requires Content Delivery Networks (CDNs) whose cost are prohibitive for many providers. An alternative is to cache and serve video content using end-users devices. Direct connections between these devices complement the resources of core VoD servers with an edge-assisted collaborative CDN. VoD access histories can reveal critical personal information, and centralized VoD solutions are notorious for exploiting personal data. Hiding the interests of users from servers and edge-assisting devices is necessary for a new generation of privacy-preserving VoD services. We introduce PrivaTube, a scalable and cost-effective VoD solution. PrivaTube aggregates video content from multiple servers and edge peers to offer a high Quality of Experience (QoE) for its users. It enables privacy preservation at all levels of the content distribution process. It leverages Trusted Execution Environments (TEEs) at servers and clients, and obfuscates access patterns using fake requests that reduce the risk of personal information leaks. Fake requests are further leveraged to implement proactive provisioning and improve QoE. Our evaluation of a complete prototype shows that PrivaTube reduces the load on servers and increases QoE while providing strong privacy guarantees