OnionPIR: Response Efficient Single-Server PIR

Mughees, Muhammad Haris; Chen, Hao; Ren, Ling

doi:10.1145/3460120.3485381

Cited by 40 publications

(8 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Subsequently, in 2018, Angel et al [4] proposed a PIR scheme called SealPIR, utilizing the BFV encryption scheme to reduce client communication overhead. In 2021, Mughees et al [31] presented OnionPIR, a PIR scheme capable of reducing server response sizes. In the same year, Ahmad et al [32] designed FastPIR, a PIR scheme that decreases server computational costs by increasing client request sizes.…”

Section: Private Information Retrieval (Pir)mentioning

confidence: 99%

A Secure Face Verification Scheme Based on Fully Homomorphic Encryption with Anonymity

Wang,

2024

Information

View full text Add to dashboard Cite

With the widespread adoption of cloud computing, the face verification process often requires the client to upload the face to an untrusted cloud server to obtain the verification results. Privacy leakage issues may arise if the client’s private information is not protected. This paper proposes a secure and anonymous face verification scheme using fully homomorphic encryption technology and SealPIR. Our scheme is a three-party solution that requires a third-party server trusted by the client. This scheme not only prevents the client’s facial data from being obtained by untrusted data servers but also prevents the data server from learning the index corresponding to the face that the client wants to verify. In a single-face verification process, the client only needs to perform one upload operation and one download operation, with a communication volume of 264 KB. We can complete a privacy-protected anonymous face verification process in 84.91 ms.

show abstract

Section: Private Information Retrieval (Pir)mentioning

confidence: 99%

A Secure Face Verification Scheme Based on Fully Homomorphic Encryption with Anonymity

Wang,

2024

Information

View full text Add to dashboard Cite

show abstract

“…S-SIP 2 is fully online without precomputing. We use the state-of-the-art PIR technology [24] as the underlying technology and extend it to the batch query mode to reduce overhead through amortization. S-SIP 2 enables the overhead of S-SIP to be linear to the client's input (smaller) and logarithmic to the size of the server's dataset.…”

Section: Our Contributionsmentioning

confidence: 99%

“…In this paper, we leverage PIR technology on the single server as an underlying building block [24]. Briefly, given a server holding a database DB of N strings, PIR enables • PIR KeyGen (1 λ ) → (pk, sk).…”

Section: Private Information Retrievalmentioning

confidence: 99%

“…If the client only needs to retrieve the entry at h i (x j ) without summing and masking, it only needs to utilize the standard symmetric PIR. Whereas in S-SIP 2 the client needs to sum the values of the k positions that the hashes map to, we use a modified version of PIR [24], named Sum-PIR.…”

Section: The S-sip 2 Constructionmentioning

confidence: 99%

“…It provides 128-bit security. We adopt OnionPIR [24] as the underlying structure for constructing S-SIP 2 . The realization of ROOM follows all the implemtation described in their paper.…”

Section: Implementation Detailsmentioning

confidence: 99%

See 2 more Smart Citations

New Secure Sparse Inner Product with Applications to Machine Learning

Xu¹,

Xu²,

Ning³

et al. 2022

Preprint

View full text Add to dashboard Cite

Sparse inner product (SIP) has the attractive property of overhead being dominated by the intersection of inputs between parties, independent of the actual input size. It has intriguing prospects, especially for boosting machine learning on large-scale data, which is tangled with sparse data. In this paper, we investigate privacy-preserving SIP problems that have rarely been explored before. Specifically, we propose two concrete constructs, one requiring offline linear communication which can be amortized across queries, while the other has sublinear overhead but relies on the more computationally expensive tool. Our approach exploits state-of-the-art cryptography tools including garbled Bloom filters (GBF) and Private Information Retrieval (PIR) as the cornerstone, but carefully fuses them to obtain non-trivial overhead reductions. We provide formal security analysis of the proposed constructs and implement them into representative machine learning algorithms including k-nearest neighbors, naive Bayes classification and logistic regression. Compared to the existing efforts, our method achieves 2-50× speedup in runtime and up to 10× reduction in communication.

show abstract