Committed Private Information Retrieval
A private information retrieval (PIR) scheme allows a client to retrieve a data item $x_i$ among $n$ items $x_1,x_2,\ldots,x_n$ from $k$ servers, without revealing what $i$ is even when $t < k$ servers collude and try to learn $i$. Such a PIR scheme is said to be $t$-private. A PIR scheme is $v$-verifiable if the client can verify the correctness of the retrieved $x_i$ even when $v \leq k$ servers collude and try to fool the client by sending manipulated data. Most of the previous works in the literature on PIR assumed that $v < k$, leaving the case of all-colluding servers open. We propose a generic construction that combines a linear map commitment (LMC) and an arbitrary linear PIR scheme to produce a $k$-verifiable PIR scheme, termed a committed PIR scheme. Such a scheme guarantees that even in the worst scenario, when all servers are under the control of an attacker, although the privacy is unavoidably lost, the client won't be fooled into accepting an incorrect $x_i$. We demonstrate the practicality of our proposal by implementing the committed PIR schemes based on the Lai-Malavolta LMC and three well-known PIR schemes using the GMP library and blst, the current fastest C library for elliptic curve pairings.
Code (1)
Tasks
Information RetrievalRetrievalMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Marital Stability With Committed Couples: A Revealed Preference Analysis
We present a revealed preference characterization of marital stability where some couples are committed. A couple is committed if they can divorce only with mutual consent. We provide theoretical insights into the potent…
Robust pricing for cloud computing
We study the robust sequential screening problem of a monopolist seller of multiple cloud computing services facing a buyer who has private information about his demand distribution for these services. At the time of con…
Cloud ComputingDon't forget private retrieval: distributed private similarity search for large language models
While the flexible capabilities of large language models (LLMs) allow them to answer a range of queries based on existing learned knowledge, information retrieval to augment generation is an important tool to allow LLMs …
Information RetrievalRetrievalRetrieval-augmented GenerationReasoning over Public and Private Data in Retrieval-Based Systems
Users and organizations are generating ever-increasing amounts of private data from a wide range of sources. Incorporating private data is important to personalize open-domain applications such as question-answering, fac…
Fact CheckingInformation RetrievalMulti-hop Question AnsweringQuestion Answering+1The Role of Reusable and Single-Use Side Information in Private Information Retrieval
This paper introduces the problem of Private Information Retrieval with Reusable and Single-use Side Information (PIR-RSSI). In this problem, one or more remote servers store identical copies of a set of $K$ messages, an…
Information RetrievalRetrieval