scholarly journals Are you The One to Share? Secret Transfer with Access Structure

2017 ◽  
Vol 2017 (1) ◽  
pp. 149-169 ◽  
Author(s):  
Yongjun Zhao ◽  
Sherman S.M. Chow
Keyword(s):  
State Of The Art ◽  
Bloom Filter ◽  
Access Structure ◽  
Polynomial Evaluation ◽  
Threshold Policy ◽  
Oblivious Polynomial Evaluation ◽  
Set Intersection ◽  
Malicious Model ◽  
Private Set Intersection ◽  
The One

Abstract Sharing information to others is common nowadays, but the question is with whom to share. To address this problem, we propose the notion of secret transfer with access structure (STAS). STAS is a twoparty computation protocol that enables the server to transfer a secret to a client who satisfies the prescribed access structure. In this paper, we focus on threshold secret transfer (TST), which is STAS for threshold policy and can be made more expressive by using linear secret sharing. TST enables a number of applications including a simple construction of oblivious transfer (OT) with threshold access control, and (a variant of) threshold private set intersection (t-PSI), which are the first of their kinds in the literature to the best of our knowledge. The underlying primitive of STAS is a variant of OT, which we call OT for a sparse array. We provide two constructions which are inspired by state-of-the-art PSI techniques including oblivious polynomial evaluation (OPE) and garbled Bloom filter (GBF). The OPEbased construction is secure in the malicious model, while the GBF-based one is more efficient. We implemented the latter one and showed its performance in applications such as privacy-preserving matchmaking.

scholarly journals SE-PSI: Fog/Cloud server-aided enhanced secure and effective private set intersection on scalable datasets with Bloom Filter

2021 ◽  
Vol 19 (2) ◽  
pp. 1861-1876
Author(s):  
Shuo Qiu ◽  
◽  
Zheng Zhang ◽  
Yanan Liu ◽  
Hao Yan ◽  
...  
Keyword(s):  
Big Data ◽  
Experimental Evaluation ◽  
High Efficiency ◽  
Bloom Filter ◽  
Medical System ◽  
Set Intersection ◽  
Cloud Server ◽  
Private Set Intersection ◽  
Enhance Efficiency ◽  
Application Requirements

<abstract><p>Private Set Intersection (PSI), which is a hot topic in recent years, has been extensively utilized in credit evaluation, medical system and so on. However, with the development of big data era, the existing traditional PSI cannot meet the application requirements in terms of performance and scalability. In this work, we proposed two secure and effective PSI (SE-PSI) protocols on scalable datasets by leveraging deterministic encryption and Bloom Filter. Specially, our first protocol focuses on high efficiency and is secure under a semi-honest server, while the second protocol achieves security on an economic-driven malicious server and hides the set/intersection size to the server. With experimental evaluation, our two protocols need only around 15 and 24 seconds respectively over one million-element datasets. Moreover, as a novelty, a <italic>multi-round</italic> mechanism is proposed for the two protocols to improve the efficiency. The implementation demonstrates that our <italic>two-round</italic> mechanism can enhance efficiency by almost twice than two basic protocols.</p></abstract>

scholarly journals Quantum private set intersection cardinality based on bloom filter

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Bai Liu ◽  
Ou Ruan ◽  
Runhua Shi ◽  
Mingwu Zhang
Keyword(s):  
Privacy Protection ◽  
Data Privacy ◽  
Communication Complexity ◽  
Single Photon ◽  
Bloom Filter ◽  
Distributed Database ◽  
Single Photons ◽  
Practical Applications ◽  
Set Intersection ◽  
Private Set Intersection

AbstractPrivate Set Intersection Cardinality that enable Multi-party to privately compute the cardinality of the set intersection without disclosing their own information. It is equivalent to a secure, distributed database query and has many practical applications in privacy preserving and data sharing. In this paper, we propose a novel quantum private set intersection cardinality based on Bloom filter, which can resist the quantum attack. It is a completely novel constructive protocol for computing the intersection cardinality by using Bloom filter. The protocol uses single photons, so it only need to do some simple single-photon operations and tests. Thus it is more likely to realize through the present technologies. The validity of the protocol is verified by comparing with other protocols. The protocol implements privacy protection without increasing the computational complexity and communication complexity, which are independent with data scale. Therefore, the protocol has a good prospects in dealing with big data, privacy-protection and information-sharing, such as the patient contact for COVID-19.

scholarly journals Two-Party Privacy-Preserving Set Intersection with FHE

Entropy ◽  
2020 ◽  
Vol 22 (12) ◽  
pp. 1339
Author(s):  
Yunlu Cai ◽  
Chunming Tang ◽  
Qiuxia Xu
Keyword(s):  
Private Information ◽  
Homomorphic Encryption ◽  
Bloom Filter ◽  
Cloud Service ◽  
Fully Homomorphic Encryption ◽  
Cloud Service Provider ◽  
Set Size ◽  
Set Intersection ◽  
Learning With Errors ◽  
Private Set Intersection

A two-party private set intersection allows two parties, the client and the server, to compute an intersection over their private sets, without revealing any information beyond the intersecting elements. We present a novel private set intersection protocol based on Shuhong Gao’s fully homomorphic encryption scheme and prove the security of the protocol in the semi-honest model. We also present a variant of the protocol which is a completely novel construction for computing the intersection based on Bloom filter and fully homomorphic encryption, and the protocol’s complexity is independent of the set size of the client. The security of the protocols relies on the learning with errors and ring learning with error problems. Furthermore, in the cloud with malicious adversaries, the computation of the private set intersection can be outsourced to the cloud service provider without revealing any private information.

scholarly journals Circuit-PSI With Linear Complexity via Relaxed Batch OPPRF

2021 ◽  
Vol 2022 (1) ◽  
pp. 353-372
Author(s):  
Nishanth Chandran ◽  
Divya Gupta ◽  
Akash Shah
Keyword(s):  
Communication Complexity ◽  
Linear Complexity ◽  
State Of The Art ◽  
The State ◽  
Communication Cost ◽  
Independent Interest ◽  
Pseudorandom Functions ◽  
Long Line ◽  
Set Intersection ◽  
Private Set Intersection

Abstract In 2-party Circuit-based Private Set Intersection (Circuit-PSI), P 0 and P 1 hold sets S0 and S1 respectively and wish to securely compute a function f over the set S0 ∩ S1 (e.g., cardinality, sum over associated attributes, or threshold intersection). Following a long line of work, Pinkas et al. (PSTY, Eurocrypt 2019) showed how to construct a concretely efficient Circuit-PSI protocol with linear communication complexity. However, their protocol requires super-linear computation. In this work, we construct concretely efficient Circuit-PSI protocols with linear computational and communication cost. Further, our protocols are more performant than the state-of-the-art, PSTY – we are ≈ 2.3× more communication efficient and are up to 2.8× faster. We obtain our improvements through a new primitive called Relaxed Batch Oblivious Programmable Pseudorandom Functions (RB-OPPRF) that can be seen as a strict generalization of Batch OPPRFs that were used in PSTY. This primitive could be of independent interest.

scholarly journals PIR-PSI: Scaling Private Contact Discovery

2018 ◽  
Vol 2018 (4) ◽  
pp. 159-178 ◽  
Author(s):  
Daniel Demmler ◽  
Peter Rindal ◽  
Mike Rosulek ◽  
Ni Trieu
Keyword(s):  
Social Networking ◽  
Private Information ◽  
Provable Security ◽  
State Of The Art ◽  
Private Information Retrieval ◽  
Social Contacts ◽  
Set Intersection ◽  
New Ideas ◽  
Private Set Intersection ◽  
Security Guarantees

Abstract An important initialization step in many social-networking applications is contact discovery, which allows a user of the service to identify which of its existing social contacts also use the service. Naïve approaches to contact discovery reveal a user’s entire set of social/professional contacts to the service, presenting a significant tension between functionality and privacy. In this work, we present a system for private contact discovery, in which the client learns only the intersection of its own contact list and a server’s user database, and the server learns only the (approximate) size of the client’s list. The protocol is specifically tailored to the case of a small client set and large user database. Our protocol has provable security guarantees and combines new ideas with state-of-the-art techniques from private information retrieval and private set intersection. We report on a highly optimized prototype implementation of our system, which is practical on real-world set sizes. For example, contact discovery between a client with 1024 contacts and a server with 67 million user entries takes 1.36 sec (when using server multi-threading) and uses only 4.28 MiB of communication.

Provably Secure Private Set Intersection With Constant Communication Complexity

2019 ◽  
Vol 9 (2) ◽  
pp. 39-64
Author(s):  
Sumit Kumar Debnath
Keyword(s):  
Communication Complexity ◽  
Bloom Filter ◽  
Security And Privacy ◽  
Data Sets ◽  
Indistinguishability Obfuscation ◽  
Security Parameter ◽  
Private Data ◽  
Set Intersection ◽  
Private Set Intersection ◽  
Probabilistic Data Structure

Electronic information is increasingly shared among unreliable entities. In this context, one interesting problem involves two parties that secretly want to determine an intersection of their respective private data sets while none of them wish to disclose the whole set to the other. One can adopt a Private Set Intersection (PSI) protocol to address this problem preserving the associated security and privacy issues. In this article, the authors present the first PSI protocol that incurs constant (p(k)) communication complexity with linear computation overhead and is fast even for the case of large input sets, where p(k) is a polynomial in security parameter k. Security of this scheme is proven in the standard model against semi-honest entities. The authors combine somewhere statistically binding (SSB) hash function with indistinguishability obfuscation (iO) and space-efficient probabilistic data structure Bloom filter to design the scheme.

Sign in / Sign up

Export Citation Format

Share Document