scholarly journals Efficiency improvement techniques for private intersection-sum protocol using Bloom filter

2022 ◽  
Vol 4 (2) ◽  
Author(s):  
Hiroyuki Kano ◽  
Keisuke Hakuta
Keyword(s):  
Data Structures ◽  
Bloom Filter ◽  
Rational Integer ◽  
Bloom Filters ◽  
Efficiency Improvement ◽  
Probabilistic Data ◽  
Set Intersection ◽  
Private Set Intersection

AbstractA private set intersection protocol is one of the secure multi-party computation protocols, and allows participants to compute the intersection of their sets without revealing them to each other. Ion et al. proposed the private intersection-sum protocol (PI-Sum). The PI-Sum is one of the two-party private set intersection protocol. In the PI-Sum, two parties (say Alice and Bob) have the private sets A and B. Moreover, Bob additionaly has a rational integer associated with each element of B. The PI-Sum allows Bob to obtain the sum of the rational integers associated with the elements of $$A \cap B$$ A ∩ B . This paper proposes the efficiency improvement techniques for the PI-Sum. The proposed techniques are based on Bloom filters which are probabilistic data structures. More precisely, this paper proposes three protocols which are modifications of the PI-Sum. The proposed protocols are more efficient than the PI-Sum.

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 Probabilistic data structures applied to implicit graph representation

2018 ◽  
Author(s):  
Juan P. A. Lopes ◽  
Fabiano S. Oliveira ◽  
Paulo E. D. Pinto
Keyword(s):  
Data Structures ◽  
Streaming Data ◽  
Space Complexity ◽  
Graph Representation ◽  
Bloom Filters ◽  
Probabilistic Data ◽  
Implicit Representation ◽  
Large Graphs ◽  
Sparse Graphs ◽  
Timely Fashion

In recent years, probabilistic data structures have been extensively employed to handle large volumes of streaming data in a timely fashion. However, their use in algorithms on giant graphs has been poorly explored. We introduce the concept of probabilistic implicit graph representation, which can represent large graphs using much less memory asymptotically by allowing adjacency test to have a constant probability of false positives or false negatives. This is an extension from the concept of implicit graph representation, comprehensively studied by Muller and Spinrad. Based on that, we also introduce two novel representations using probabilistic data structures. The first uses Bloom filters to represent general graphs with the same space complexity as the adjacency matrix (outperforming it however for sparse graphs). The other uses MinHash to represent trees with lower space complexity than any deterministic implicit representation. Furthermore, we prove some theoretical limitations for the latter approach.

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 Creating a Concurrent Overflowing Bloom Filter

2019 ◽  
Author(s):  
Alex Berliner ◽  
Brian Estes ◽  
Ebin Scaria
Keyword(s):  
Data Structure ◽  
Recent Literature ◽  
Bloom Filter ◽  
Bloom Filters ◽  
Probabilistic Data ◽  
Additional Element ◽  
Marginal Value ◽  
The Creation ◽  
Probabilistic Data Structure

Bloom filters are an efficient probabilistic data structure used to verify membership of an element inside of a set. There is diminishing marginal value for inserting each additional element into a Bloom filter, and so steps must be taken to maintain scalability. One such option is to create a secondary hash set for a particular hash set in a Bloom filter that has become full, known as an overflow area. At this time, there are no implementations of a Bloom filter that implement this overflow system while maintaining concurrency. In this paper, we demonstrate the creation of a concurrent overflow system for Bloom filters. We use the base Bloom filter presented in recent literature and replace their method of dynamically resizing the Bloom filters with our overflow table implementation, as outlined in one of their suggested areas for future exploration. We then compare the results of our Bloom filter with those from the previously mentioned implementation as well as a standard Bloom filter.

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.

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.

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