Recent works in amortized sublinear Private Information Retrieval (PIR) have demonstrated great potential. Despite the inspiring progress, existing schemes in this new paradigm are still faced with various challenges and bottlenecks, including large client storage, high communication, poor practical efficiency, need for non-colluding servers, or restricted client query sequences. We present simple and practical amortized sublinear stateful private information retrieval schemes without these drawbacks using new techniques in hint construction and usage. In particular, we introduce a dummy set to the client's request to eliminate any leakage or correctness failures. Our techniques can work with two non-colluding servers or a single server. The resulting PIR schemes achieve practical efficiency. The online response overhead is only twice that of simply fetching the desired entry without privacy. For a database with 2^28 entries of 32-byte, each query of our two-server scheme consumes 34 KB of communication and 2.7 milliseconds of computation, and each query of our single-server scheme consumes amortized 47 KB of communication and 4.5 milliseconds of computation. These results are one or more orders of magnitude better than prior works.

This paper studies Batch Private Information Retrieval (BatchPIR), a variant of private information retrieval (PIR) where the client wants to retrieve multiple entries from the server in one batch. BatchPIR matches the use case of many practical applications and holds the potential for substantial efficiency improvements over PIR in terms of amortized cost per query. Existing BatchPIR schemes have achieved decent computation efficiency but have not been able to improve communication efficiency at all. Using vectorized homomorphic encryption, we present the first BatchPIR protocol that is efficient in both computation and communication for a variety of database configurations. Specifically, to retrieve a batch of 256 entries from a database with one million entries of 256 bytes each, the communication cost of our scheme is 7.5x to 98.5x better than state-of-the-art solutions.

This paper presents OnionPIR and stateful OnionPIR, two single-server PIR schemes that significantly improve the response size and computation cost over state-of-the-art schemes. OnionPIR scheme utilizes recent advances in somewhat homomorphic encryption (SHE) and carefully composes two lattice-based SHE schemes and homomorphic operations to control the noise growth and response size. Stateful OnionPIR uses a technique based on the homomorphic evaluation of copy networks. OnionPIR achieves a response overhead of just 4.2x over the insecure baseline, in contrast to the 100x response overhead of state-of-the-art schemes. Our stateful OnionPIR scheme improves upon the recent stateful PIR framework of Patel et al. and drastically reduces its response overhead by avoiding downloading the entire database in the offline stage. Compared to stateless OnionPIR, Stateful OnionPIR reduces the computation cost by 1.8~x for different database sizes.

Abstract The rise of ad-blockers is viewed as an economic threat by online publishers who primarily rely on online advertising to monetize their services. To address this threat, publishers have started to retaliate by employing anti ad-blockers, which scout for ad-block users and react to them by pushing users to whitelist the website or disable ad-blockers altogether. The clash between ad-blockers and anti ad-blockers has resulted in a new arms race on the Web. In this paper, we present an automated machine learning based approach to identify anti ad-blockers that detect and react to ad-block users. The approach is promising with precision of 94.8% and recall of 93.1%. Our automated approach allows us to conduct a large-scale measurement study of anti ad-blockers on Alexa top-100K websites. We identify 686 websites that make visible changes to their page content in response to ad-block detection. We characterize the spectrum of different strategies used by anti ad-blockers. We find that a majority of publishers use fairly simple first-party anti ad-block scripts. However, we also note the use of third-party anti ad-block services that use more sophisticated tactics to detect and respond to ad-blockers.