Private Set Intersection Protocol Research Articles

Summation-based Private Segmented Membership Test from Threshold-Fully Homomorphic Encryption

In many real-world scenarios, there are cases where a client wishes to check if a data element they hold is included in a set segmented across a large number of data holders. To protect user privacy, the client's query and the data holders' sets should remain encrypted throughout the whole process. Prior work on Private Set Intersection (PSI), Multi-Party PSI (MPSI), Private Membership Test (PMT), and Oblivious RAM (ORAM) falls short in this scenario in many ways. They either require data holders to possess the sets in plaintext, incur prohibitively high latency for aggregating results from a large number of data holders, leak the information about the party holding the intersection element, or induce a high false positive. This paper introduces the primitive of a Private Segmented Membership Test (PSMT). We give a basic construction of a protocol to solve PSMT using a threshold variant of approximate-arithmetic homomorphic encryption and show how to overcome existing challenges to construct a PSMT protocol without leaking information about the party holding the intersection element or false positives for a large number of data holders ensuring IND-CPA^D security. Our novel approach is superior to existing state-of-the-art approaches in scalability with regard to the number of supported data holders. This is enabled by a novel summation-based homomorphic membership check rather than a product-based one, as well as various novel ideas addressing technical challenges. Our PSMT protocol supports many more parties (up to 4096 in experiments) compared to prior related work that supports only around 100 parties efficiently. Our experimental evaluation shows that our method's aggregation of results from data holders can run in 92.5s for 1024 data holders and a set size of 2^25, and our method's overhead increases very slowly with the increasing number of senders. We also compare our PSMT protocol to other state-of-the-art PSI and MPSI protocols and discuss our improvements in usability with a better privacy model and a larger number of parties.

Efficient Cryptographic Solutions for Unbalanced Private Set Intersection in Mobile Communication

Private Set Intersection (PSI) is a cryptographic method in secure multi-party computation that allows entities to identify common elements in their datasets without revealing their private data. Traditional approaches assume similar-sized datasets and equal computational power, overlooking practical imbalances. In real-world applications, dataset sizes and computational capacities often vary, particularly in the Internet of Things and mobile scenarios where device limitations restrict computational types. Traditional PSI protocols are inefficient here, as computational and communication complexities correlate with the size of larger datasets. Thus, adapting PSI protocols to these imbalances is crucial. This paper explores unbalanced PSI scenarios where one party (the receiver) has a relatively small dataset and limited computational power, while the other party (the sender) has a large amount of data and strong computational capabilities. It introduces three innovative solutions for unbalanced PSI: an unbalanced PSI protocol based on the Cuckoo filter, an unbalanced PSI protocol based on single-cloud assistance, and an unbalanced PSI protocol based on dual-cloud assistance, with each subsequent solution addressing the shortcomings of the previous one. Depending on performance and security needs, different protocols can be employed for applications such as private contact discovery.

Efficient quantum secure multi-party greatest common divisor protocol and its applications in private set operations

Private set intersection (PSI) has important application value, however, current quantum PSI protocols are either unsuitable for multi-party scenarios or inefficient. Recently, Imran (arXiv:2303.17196v3, 2023) proposed two quantum secure multi-party greatest common divisor (GCD) protocols that can be used for PSI, but with the downside of information leakage and resource consumption. In this paper, we propose a novel quantum secure multi-party GCD protocol that has higher security and lower complexity. To hide privacy, each party randomly selects a coefficient within a range determined by his input integer, and with the assistance of a semi-honest third party TP, all parties secretly calculate the linear combination of their inputs under these coefficients. Once enough linear combinations are collected, TP calculates the GCD of these combinations, which is equal to the GCD of all input integers. To verify the honesty of participants, a quantum zero-knowledge proof sub-protocol is designed. Analysis shows that our GCD protocol is correct and has security against malicious attacks. Moreover, its complexity is polynomial level and lower than Imran’s. Furthermore, we demonstrate the scalability of our GCD protocol in private set operations, such as private set intersection, private set intersection cardinality, private multi-set intersection, etc.

Quantum multi-party private set intersection using single photons

Multi-party private set intersection (MP-PSI), an essential branch of secure multiparty computation (SMC), enables multiple users to collaboratively determine the intersection of their private sets without revealing any private information except for the set intersection. Although MP-PSI has many practical applications, none of the existing MP-PSI protocols are quantum-proof security. Compared to classical and post-quantum cryptography, quantum cryptography, which relies on quantum mechanics, can provide a superior solution to resist various quantum attacks and achieve quantum-proof security. In this paper, we propose a multi-party quantum private set intersection (MP-QPSI) protocol, which can compute the set intersection with one protocol execution. The proposed protocol can not only keep the private sets undisclosed to the participants and third party (TP), but also prevent external attacks from outsider eavesdroppers. It utilizes easy-to-implement technologies such as single photons, single-particle measurements, and rotation operations. To validate the feasibility of the proposed protocol, a quantum circuit for the simulation experiment is designed and executed on IBM Quantum Experience. Compared to previous studies, our protocol demonstrates improved performance in terms of security and feasibility.

Efficient multi-party PSI and its application in port management

Private Set Intersection (PSI) technology is a cryptographic tool that allows the parties holding private data to determine the intersection of sets in a joint computation without revealing any additional privacy information. As a critical component of secure multi-party computation, this technology has been widely used in the security domain of artificial intelligence and data mining. With the emergence of the era of multi-source data sharing, protocols for private set intersection computation applicable to multiple participants have also emerged. However, the performance of existing multi-party private set intersection (MPSI) protocols is suboptimal when some participants use devices with limited communication or computational capabilities, such as mobile devices. To overcome the above issues, we design a cloud-aided multi-party private set intersection protocol (Cloud-Aided-MPSI) based on oblivious programmable pseudorandom functions (OPPRF) and oblivious key–value stores (OKVS). Due to the protocol efficiently outsourcing partial computational and communication tasks to cloud servers, our performance has been further enhanced compared to existing work. Through the Cloud-Aided-MPSI protocol, we propose a port scheduling protocol for coordinated management scenarios of ships arriving and departing from ports. The protocol effectively addresses the privacy protection concerns of port management when scheduling the arrival and departure of ships. We analyze the performance of this protocol.

New approach for efficient malicious multiparty private set intersection

The Multiparty Private Set Intersection (MPSI) protocol allows untrusted parties to compute the intersection of their sets without revealing any additional information. MPSI has found wide applications in fields such as advertising, vertical federated learning, privacy-preserving data mining, and private contact tracing. However, existing MPSI solutions face challenges in terms of security and efficiency. Some MPSI schemes are secure only against semi-honest adversaries and cannot effectively defend against malicious adversaries. Other schemes, while capable of withstanding malicious adversaries, exhibit efficiency problems, especially as the number of corrupt participants increases. A practical MPSI solution must maintain high security and efficiency, even when handling large-scale data and a majority of corrupted participants.This paper introduces a novel MPSI scheme named TH-PSI, built upon a new star-shaped topological network communication framework based on the trusted execution environment. TH-PSI has been rigorously proven to be secure within the standard Universal Composability framework, capable of thwarting malicious adversaries. To demonstrate the efficiency of TH-PSI, we conduct a comprehensive series of experiments. We implement the TH-PSI scheme and tested it in both LAN and WAN settings, involving up to 30 participants and datasets as large as 222. We compare TH-PSI with state-of-the-art MPSI schemes, ENOC21 and NTY21, with a specific focus on their efficiency in the presence of malicious adversaries. Our experiments show that TH-PSI is an efficient solution capable of delivering results within a matter of seconds, even when dealing with data at the million-level and a majority of corrupted participants.

Blockchain-Based Unbalanced PSI with Public Verification and Financial Security

Private set intersection (PSI) enables two parties to determine the intersection of their respective datasets without revealing any information beyond the intersection itself. This paper particularly focuses on the scenario of unbalanced PSI, where the sizes of datasets possessed by the parties can significantly differ. Current protocols for unbalanced PSI under the malicious security model exhibit low efficiency, rendering them impractical in real-world applications. By contrast, most efficient unbalanced PSI protocols fail to guarantee the correctness of the intersection against a malicious server and cannot even ensure the client’s privacy. The present study proposes a blockchain-based unbalanced PSI protocol with public verification and financial security that enables the client to detect malicious behavior from the server (if any) and then generate an irrefutable and publicly verifiable proof without compromising its secret. The proof can be verified through smart contracts, and some economic incentive and penalty measures are executed automatically to achieve financial security. Furthermore, we implement the proposed protocol, and experimental results demonstrate that our scheme exhibits low online communication complexity and computational overhead for the client. At the same time, the size of the generated proof and its verification complexity are both O(logn), enabling cost-effective validation on the blockchain.

Blockchain Interoperability in Data Exchange Logistics Integration

Logistics companies are increasingly adopting private blockchains for enhanced data management because of the trends in cooperation. Nevertheless, this practice poses new challenges concerning the security and sharing of data. The real-time nature and diversity of logistics data increase the difficulty of protecting the data. Additionally, when transportation information changes, downstream enterprises must promptly adjust their production plans to accommodate these alterations. The strict access controls of private blockchains can obstruct downstream enterprises from obtaining data, posing a challenge to the overall operational efficiency. In this paper, we propose an innovative logistics data protection scheme that employs private set intersection (PSI) and blockchain cross-chain technology to achieve data security. In our scheme, logistics companies within the logistics consortium are added as trusted agents to the public blockchain, enabling downstream enterprises to acquire logistics data integration from the public blockchain. Utilizing an RSA-based PSI protocol, our approach enhances exchange efficiency while protecting private data without transmitting additional information. We evaluate the performance of the proposed solution through a series of experiments, and the results demonstrate that our solution can achieve secure and efficient logistics data exchange.

NEW THRESHOLD PRIVATE SET INTERSECTION PROTOCOLS

With the rising amount of digital technologies that we use on a daily basis, it is more important than ever to handle and process private data securely. Research and academic communities are becoming increasingly interested in multi-party computation, with a focus on the field of Private Set Intersection (PSI). In this regard, this work introduces a novel technique that successfully converts the Cid-Davidson Private Set Intersection protocol into a Threshold Private Set Intersection. It achieves this conversion by introducing two new protocols, TPSI-1 and TPSI-2, and utilizing two previously developed methodologies while the Reed-Solomon codes and the Shamir-secret sharing scheme are the foundations of TPSI-1, whereas Secure Comparison Protocols serve as the foundation for TPSI-2. Specifically, our suggested protocols perform better asymptotically than previous threshold PSI protocols because they have a fixed number of rounds and linear communication and computation complexity that increase with data set size. This study adds to the continuous effort to strengthen the security and effectiveness of private data calculations, highlighting how safe data processing is changing in an era where digital technologies are ingrained in every aspect of our lives.

Optimized verifiable delegated private set intersection on outsourced private datasets

Private Set Intersection (PSI) has been applied in various fields, such as human genome research, advertising conversion rate analysis, etc. Traditional PSI has the drawback of requiring local devices to be constantly online and needing high storage capacity of the devices. To overcome these issues, many researchers have focused on delegating PSI computation to cloud servers. However, third-party clouds may bring many new challenges. One of the primary concerns is ensuring that the cloud server does not obtain sensitive data when the client outsources their dataset. Additionally, ensuring clients verify the correctness of the results is also a critical issue. In this paper, we propose the optimized verifiable delegated private set intersection protocol on outsourced private datasets (VO-PSI). Our protocol enables clients to verify the correctness of intersection with a negligible probability of incorrect results while being able to guarantee data privacy. Compared with previous research, the verifiability of the protocol has been dramatically improved. We analyze the security of the protocol under the malicious model and conduct experiments to evaluate its efficiency and feasibility.

Enabling privacy-preserving medication analysis in distributed EHR systems

The digitalization of the medical field has resulted in a vast amount of medical data stored in Electronic Health Record (EHR) systems. Among the various valuable applications of EHRs, medication efficacy analysis stands out as a powerful tool for drug development and enhancing patient treatment. This analysis relies on multi-dimensional medical data, encompassing various factors. Currently, medical research is increasingly inclined towards inter-institutional sharing of medical data to enhance the accuracy and reliability of analysis results. However, due to the sensitivity of personal medical data, privacy and security considerations must be taken into account when conducting medical analyses across institutions. In this paper, we present a novel privacy-preserving scheme for distributed medication efficacy analysis. Our work allows for the analysis of multi-dimensional medical data by leveraging the private set intersection protocol and the Paillier cryptosystem. To validate the feasibility of our scheme, we implement it using some real medical datasets. Experimental results demonstrate that our approach offers enhanced security and lower computational and communication overhead compared to the original approach, which solely processes single-dimensional data at a time.

Low Communication-Cost PSI Protocol for Unbalanced Two-Party Private Sets

Two-party private set intersection (PSI) plays a pivotal role in secure two-party computation protocols. The communication cost in a PSI protocol is normally influenced by the sizes of the participating parties. However, for parties with unbalanced sets, the communication costs of existing protocols mainly depend on the size of the larger set, leading to high communication cost. In this paper, we propose a low communication-cost PSI protocol designed specifically for unbalanced two-party private sets, aiming to enhance the efficiency of communication. For each item in the smaller set, the receiver queries whether it belongs to the larger set, such that the communication cost depends solely on the smaller set. The queries are implemented by private information retrieval which is constructed with trapdoor hash function. Our investigation indicates that in each instance of invoking the trapdoor hash function, the receiver is required to transmit both a hash key and an encoding key to the sender, thus incurring significant communication cost. In order to address this concern, we propose the utilization of a seed hash key, a seed encoding key, and a Latin square. By employing these components, the sender can autonomously generate all the necessary hash keys and encoding keys, obviating the multiple transmissions of such keys. The proposed protocol is provably secure against a semihonest adversary under the Decisional Diffie–Hellman assumption. Through implementation demonstration, we showcase that when the sizes of the two sets are 28 and 214, the communication cost of our protocol is only 3.3% of the state-of-the-art protocol and under 100 Kbps bandwidth, we achieve 1.46x speedup compared to the state-of-the-art protocol. Our source code is available on GitHub: https://github.com/TAN-OpenLab/Unbanlanced-PSI.

Enabling Threshold Functionality for Private Set Intersection Protocols in Cloud Computing

Enabling Threshold Functionality for Private Set Intersection Protocols in Cloud Computing

A Practical Multiparty Private Set Intersection Protocol Based on Bloom Filters for Unbalanced Scenarios

Multiparty Private Set Intersection (MPSI) is dedicated to finding the intersection of datasets of multiple participants without disclosing any other information. Although many MPSI protocols have been presented, there are still some important practical scenarios that require in-depth consideration such as an unbalanced scenario, where the server’s dataset is much larger than the clients’ datasets, and in cases where the number of participants is large. This paper proposes a practical MPSI protocol for unbalanced scenarios. The protocol uses the Bloom filter, an efficient data structure, and the ElGamal encryption algorithm to reduce the computation of clients and the server; adopts randomization technology to solve the encryption problem of the 0s in the Bloom filter; and introduces the idea of the Shamir threshold secret-sharing scheme to adapt to multiple environments. A formal security proof and three detailed experiments are given. The results of the experiments showed that the new protocol is very suitable for unbalanced scenarios with a large number of participants, and it has a significant improvement in efficiency compared with the typical related protocol (TIFS 2022).

Practical and Malicious Multiparty Private Set Intersection for Small Sets

Private set intersection (PSI) is a pivotal subject in the realm of privacy computation. Numerous research endeavors have concentrated on situations involving vast and imbalanced sets. Nevertheless, there is a scarcity of existing PSI protocols tailored for small sets. Those that exist are either restricted to interactions between two parties or necessitate resource-intensive homomorphic operations. To bring forth practical multiparty private set intersection solutions for small sets, we present two multiparty PSI protocols founded on the principles of Oblivious Key–Value Stores (OKVSs), polynomials, and gabled cuckoo tables. Our security analysis underscores the resilience of these protocols against malicious models and collision attacks. Through experimental evaluations, we establish that, in comparison to related endeavors, our protocols excel in small-set contexts, particularly in low-bandwidth wide area network (WAN) settings.

Private Set Intersection Based on Lightweight Oblivious Key-Value Storage Structure

At this stage, the application of Private Set Intersection (PSI) protocols is essential for smart homes. Oblivious Key-Value Stores (OKVS) can be used to design efficient PSI protocols. Constructing OKVS with a cuckoo hashing graph is a common approach. It increases the number of hash functions while reducing the possibility of collisions into rings. However, the existing OKVS construction scheme requires a high time overhead, and such an OKVS applied to PSI protocols would also have a high communication overhead. In this paper, we propose a method called 3-Hash Garbled Cuckoo Graph (3H-GCG) for constructing cuckoo hash graphs. Specifically, this method handles hash collisions between different keys more efficiently than existing methods, and it can also be used to construct an OKVS structure with less storage space. Based on the 3H-GCG, we design a PSI protocol using the Vector Oblivious Linear Evaluation (VOLE) and OKVS paradigm, which achieves semi-honest security and malicious security. Extensive experiments demonstrate the effectiveness of our method. When the set size is 218–220, our PSI protocol is less computationally intensive than other existing protocols. The experiments also show an increase in the ratio of raw to constructed data of about 7.5%. With the semi-honest security setting, our protocol achieves the fastest runtime with the set size of 218. With malicious security settings, our protocol has about 10% improvement in communication compared with other existing protocols.

IHVFL: a privacy-enhanced intention-hiding vertical federated learning framework for medical data

Vertical Federated Learning (VFL) has many applications in the field of smart healthcare with excellent performance. However, current VFL systems usually primarily focus on the privacy protection during model training, while the preparation of training data receives little attention. In real-world applications, like smart healthcare, the process of the training data preparation may involve some participant’s intention which could be privacy information for this participant. To protect the privacy of the model training intention, we describe the idea of Intention-Hiding Vertical Federated Learning (IHVFL) and illustrate a framework to achieve this privacy-preserving goal. First, we construct two secure screening protocols to enhance the privacy protection in feature engineering. Second, we implement the work of sample alignment bases on a novel private set intersection protocol. Finally, we use the logistic regression algorithm to demonstrate the process of IHVFL. Experiments show that our model can perform better efficiency (less than 5min) and accuracy (97%) on Breast Cancer medical dataset while maintaining the intention-hiding goal.

A Novel Edge Cache-Based Private Set Intersection Protocol via Lightweight Oblivious PRF.

With the rapid development of edge computing and the Internet of Things, the problem of information resource sharing can be effectively solved through multi-party collaboration, but the risk of data leakage is also increasing. To address the above issues, we propose an efficient multi-party private set intersection (MPSI) protocol via a multi-point oblivious pseudorandom function (OPRF). Then, we apply it to work on a specific commercial application: edge caching. The proposed MPSI uses oblivious transfer (OT) together with a probe-and-XOR of strings (PaXoS) as the main building blocks. It not only provides one-sided malicious security, but also achieves a better balance between communication and computational overhead. From the communication pattern perspective, the client only needs to perform OT with the leader and send a data structure PaXoS to the designated party, making the protocol extremely efficient. Moreover, in the setting of edge caching, many parties hold a set of items containing an identity and its associated value. All parties can identify a set of the most frequently accessed common items without revealing the underlying data.

Topgun: An ECC Accelerator for Private Set Intersection

Elliptic Curve Cryptography (ECC), one of the most widely used asymmetric cryptographic algorithms, has been deployed in Transport Layer Security (TLS) protocol, blockchain, secure multiparty computation, and so on. As one of the most secure ECC curves, Curve25519 is employed by some secure protocols, such as TLS 1.3 and Diffie-Hellman Private Set Intersection (DH-PSI) protocol. High-performance implementation of ECC is required, especially for the DH-PSI protocol used in privacy-preserving platform. Point multiplication, the chief cryptographic primitive in ECC, is computationally expensive. To improve the performance of DH-PSI protocol, we propose Topgun, a novel and high-performance hardware architecture for point multiplication over Curve25519. The proposed architecture features a pipelined Finite-field Arithmetic Unit and a simple and highly efficient instruction set architecture. Compared to the best existing work on Xilinx Zynq 7000 series FPGA, our implementation with one Processing Element can achieve 3.14× speedup on the same device. To the best of our knowledge, our implementation appears to be the fastest among the state-of-the-art works. We also have implemented our architecture consisting of 4 Compute Groups, each with 16 PEs, on an Intel Agilex AGF027 FPGA. The measured performance of 4.48 Mops/s is achieved at the cost of 86 Watts power, which is the record-setting performance for point multiplication over Curve25519 on FPGAs.

An agent-based secure privacy-preserving decentralized protocol for sharing and managing digital health passport information during crises.

The aim of this article is to identify a range of changes and challenges that present-day technologies often present to contemporary societies, particularly in the context of smart city logistics, especially during crises. For example, the long-term consequences of the COVID-19 pandemic, such as life losses, economic damages, and privacy and security violations, demonstrate the extent to which the existing designs and deployments of technological means are inadequate. The article proposes a privacy-preserving, decentralized, secure protocol to safeguard individual boundaries and supply governments and public health organizations with cost-effective information, particularly regarding vaccination. The contribution of this article is threefold: (i) conducting a systematic review of most of the privacy-preserving apps and their protocols created during pandemics, and we found that most apps pose security and privacy violations. (ii) Proposing an agent-based, decentralized private set intersection (PSI) protocol for securely sharing individual digital personal and health passport information. The proposed scheme is called secure mobile digital passport agent (SMDPA). (iii) Providing a simulation measurement of the proposed protocol to assess performance. The performance result proves that SMDPA is a practical solution and better than the proposed active data bundles using secure multi-party computation (ADB-SMC), as the average CPU load for SMDPA is approximately 775 milliseconds (ms) compared to about 900 ms for ADB-SMC.