Private information retrieval (PIR) is a key building block in many privacy-preserving systems. Unfortunately, existing constructions remain very expensive. This paper introduces two techniques that make the computational variant of PIR (CPIR) more efficient in practice. The first technique targets a recent class of CPU-efficient CPIR protocols where the query sent by the client contains a number of ciphertexts proportional to the size of the database. We show how to compresses this query, achieving size reductions of up to 274×.The second technique is a new data encoding called probabilistic batch codes (PBCs). We use PBCs to build a multi-query PIR scheme that allows the server to amortize its computational cost when processing a batch of requests from the same client. This technique achieves up to 40× speedup over processing queries one at a time, and is significantly more efficient than related encodings. We apply our techniques to the Pung private communication system, which relies on a custom multi-query CPIR protocol for its privacy guarantees. By porting our techniques to Pung, we find that we can simultaneously reduce network costs by 36× and increase throughput by 3×.
DRP is a new concurrency control protocol for software transactional memory that achieves high throughput, even for skewed workloads that exhibit high contention. DRP builds on prior works that chop transactions into pieces to expose more concurrency opportunities, but unlike these works, DRP performs no static analyses and supports arbitrary workloads. DRP achieves a high degree of concurrency across most workloads and guarantees deadlock freedom, strict serializability, and opacity. We incorporate DRP into the software transactional objects library STO [18] and find that DRP improves STO's throughput on several STAMP benchmarks by up to 3.6×. Additionally, an in-memory multicore database implemented with our modified variant of STO outperforms databases that use OCC or transaction chopping for concurrency control. Specifically, DRP achieves 6.6× higher throughput than OCC when contention is high. Compared to transaction chopping, DRP achieves 3.3× higher throughput when contention is medium or low. Furthermore, our implementation achieves comparable performance to OCC and transaction chopping at other contention levels.
This paper treats a critical component of the Web ecosystem that has so far received little attention in our community: ad exchanges. Ad exchanges run auctions to sell publishers' inventory-space on Web pages-to advertisers who want to display ads in those spaces. Unfortunately, under the status quo, the parties to an auction cannot check that the auction was carried out correctly, which raises the following more general question: how can we create verifiability in low-latency, high-frequency auctions where the parties do not know each other? We address this question with the design, prototype implementation, and experimental evaluation of VEX. VEX introduces a technique for efficient, privacy-preserving integer comparisons; couples these with careful protocol design; and adds little latency and tolerable overhead.
Resource disaggregation is a new architecture for data centers in which resources like memory and storage are decoupled from the CPU, managed independently, and connected through a high-speed network. Recent work has shown that although disaggregated data centers (DDCs) provide operational benefits, applications running on DDCs experience degraded performance due to extra network latency between the CPU and their working sets in main memory. DBMSs are an interesting case study for DDCs for two main reasons: (1) DBMSs normally process data-intensive workloads and require data movement between different resource components; and (2) disaggregation drastically changes the assumption that DBMSs can rely on their own internal resource management. We take the first step to thoroughly evaluate the query execution performance of production DBMSs in disaggregated data centers. We evaluate two popular open-source DBMSs (MonetDB and PostgreSQL) and test their performance with the TPC-H benchmark in a recently released operating system for resource disaggregation. We evaluate these DBMSs with various configurations and compare their performance with that of single-machine Linux with the same hardware resources. Our results confirm that significant performance degradation does occur, but, perhaps surprisingly, we also find settings in which the degradation is minor or where DDCs actually improve performance.
This paper introduces a new attack on recent messaging systems that protect communication metadata. The main observation is that if an adversary manages to compromise a user's friend, it can use this compromised friend to learn information about the user's other ongoing conversations. Specifically, the adversary learns whether a user is sending other messages or not, which opens the door to existing intersection and disclosure attacks. To formalize this compromised friend attack, we present an abstract scenario called the exclusive call center problem that captures the attack's root cause, and demonstrates that it is independent of the particular design or implementation of existing metadata-private messaging systems. We then introduce a new primitive called a private answering machine that can prevent the attack. Unfortunately, building a secure and efficient instance of this primitive under only computational hardness assumptions does not appear possible. Instead, we give a construction under the assumption that users can bound their maximum number of friends and are okay leaking this information.
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