Private Set Intersection Protocol Research Articles

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.

Efficient multi-party private set intersection protocols for large participants and small sets

Private set intersection (PSI) has attracted the researchers and the developers both from academia and industry in cooperating the private data to achieve privacy preserving. Traditional PSI protocols allow the two participants with each private sets interactively calculating the intersection without revealing any additional information. Multi-party private set intersection (MP-PSI) is quite different from the conventional two-party PSI, which could not directly obtain from the two-party PSI, and become an emerging topic in collaborative data analytics from different private data owners. However, the most of the existing MP-PSI protocols always consider the small number of the participants with large set size and the performance drops with the increasing number of participants. In addition, the participants with small set size also bring the heavy communication and computation overhead. To overcome the above drawbacks, we propose two efficient MP-PSI protocols for large number of participants and small set size, and formally prove secure against collision attack in the semi-honest model and malicious model. The experiments show that when the participant number increases from 5 to 50 and the set size scales from 27 to 210, our protocols are faster than the most efficient MP-PSI protocol. Thus, our MP-PSI protocols are considering to be practical solutions, which fit the scenarios in small set size with increasing participants.

Securely Computing Protocol of Set Intersection under the Malicious Model

Private set intersection (PSI) is a valuable technique with various practical applications, including secure matching of communication packets in the Internet of Things. However, most of the currently available two-party PSI protocols are based on the oblivious transfer (OT) protocol, which is computationally expensive and results in significant communication overhead. In this paper, we propose a new coding method to design a two-party PSI protocol under the semi-honest model. We analyze possible malicious attacks and then develop a PSI protocol under the malicious model using the Paillier cryptosystem, cut-and-choose, zero-knowledge proof, and other cryptographic tools. By adopting the real/ideal model paradigm, we prove the protocol’s security under the malicious model, which is more efficient compared to the existing related schemes.

Toward Privacy-Preserving Directly Contactable Symptom-Matching Scheme for IoT Devices

The development of IoT devices has driven technological advancements across industries, especially in healthcare. IoT devices have brought many conveniences to patients, such as symptom matching, the real-time acquisition of health data, and online diagnosis. However, the development of the Internet of Things also brings security and privacy challenges, which have attracted the attention of many scholars. In symptom matching, patients can communicate with patients similar to themselves through symptom matching, exchange treatment experiences, and encourage each other. However, matching in plaintext will pose a huge threat to user privacy, such as discrimination, which in turn affects job hunting, etc. Therefore, this paper proposes a symptom-matching scheme for IoT devices based on the Diffie–Hellman key agreement. Specifically, we construct and formally define the Switching Threshold Label Private Set Intersection (STLPSI) protocol based on the Diffie–Hellman key agreement and apply it for medical symptom matching. Our protocol can not only set the threshold of the same symptoms, but also patients who meet the threshold can obtain one another’s contact information. Furthermore, our scheme does not rely on any trusted third parties. Through security analysis and experiments, our scheme is shown to be effective in preserving privacy during symptom matching.

Multi-party Privacy Set Intersection protocol with an Untrusted Cloud Server

The development of cloud computing has brought many conveniences to life, its advantages of low cost and high computing power bring convenience to our life, but the main problem is how to protect user data on untrustworthy cloud servers. To solve this problem, we have proposed a new private set intersection protocol, denoted as. The protocol uses a representation of sets with polynomial point-value pairs. All elements should be transmitted and computed in the protocol as ciphertext by introducing random polynomials to re-randomize the encryption elements. To reduce the computational burden of each party. The parties send the processed polynomials to the cloud server, which calculates the intersection. In addition, our protocol achieves fairness in the case of collusion between parties. Finally, the security of the protocol is demonstrated under the UC model. Comparing the performance of this protocol with other related protocols show that the protocol is better than the other associated protocols in terms of low computational overhead.

Adaptively code-correlation robustness functions and its applications to private set intersection

In this paper, we propose a novel concept of adaptively code-correlation robustness functions (CCRFs) and construct this primitive from public random permutation. The security of our construction can be proved by H-Coefficient technique. Furthermore, the public random permutation can be instantiated by some lightweight designs like ChaCha20 512-bit permutation which is faster than SHA-512 and BLAKE2.We also give a one-sided malicious private set intersection (PSI) protocol in Universal Composability (UC) framework without random oracle model (ROM) assumption based on the adaptively CCRFs. In the high bandwidth setting, our implementation is nearly as fast as state-of-the-art semi-honest KKRT protocol, and sometimes even faster than it, owing to the efficiency of our instantiation of adaptively CCRFs, which may be of independent interest used in other secure protocols.

Multiparty Threshold Private Set Intersection Protocol with Low Communication Complexity

Multiparty threshold private set intersection (MP-TPSI) protocol allows n mutually untrusted parties P 1 , P 2 , … , P n holding data sets A 1 , A 2 , … , A n of size m respectively to jointly compute the intersection I = A 1 ∩ A 2 ∩ ⋯ ∩ A n over all their private data sets only if the size of intersection is larger than m − t , while ensuring that no other private information of the data sets other than the intersection is revealed, where t is the threshold. In the MP-TPSI protocol, multiple parties first decide whether the size of the intersection is larger than the threshold t ; then, they compute the intersection if the size of the intersection is larger than the threshold t . However, the existing MP-TPSI protocols use different forms of evaluation polynomials in the cardinality testing and intersection computing phases, so that parties need to transmit and calculate a large number of evaluation values, which leads to high communication and computational complexity. In addition, the existing MP-TPSI protocols cannot guarantee the security and the correctness of the results, that is, an adversary can know the additional information beyond the intersection, and the elements that are not in the intersection are calculated as the intersection. To solve these issues, based on the threshold fully homomorphic encryption (TFHE) and sparse polynomial interpolation, we propose an MP-TPSI protocol. In the star network topology, the theoretical communication complexity of the proposed MP-TPSI protocol depends on the threshold t and the number of parties n , not on the size of set m . Moreover, the proposed MP-TPSI protocol outperforms other related MP-TPSI protocols in terms of computational and communication overheads. Furthermore, the proposed MP-TPSI protocol tolerates up to n − 1 corrupted parties in the semi-honest model, where no set of colluding parties can learn the input of an honest party in the strictest dishonest majority setting.