Optimized verifiable delegated private set intersection on outsourced private datasets

This paper introduces certified sets to the private set intersection problem. A private set intersection protocol allows Alice and Bob to jointly compute the set intersection function without revealing their input sets. Since the inputs are private, malicious participants may choose their sets arbitrarily and may use this flexibility to affect the result or learn more about the input of an honest participant. With certified sets, a trusted party ensures the inputs are valid and binds them to each participant. The strength of the malicious model with certified inputs increases the applicability of private set intersection to real world problems. With respect to efficiency the new certified set intersection protocol improves existing malicious model private set intersection protocols by a constant factor.Keywordsprivate set intersectionsecure two-party computationcertified sets

A private set intersection (PSI) protocol enables two parties to privately compute the intersection of their inputs. Most of its previous versions are unilateral, that is, only one party can learn the intersection and the other learns nothing. Many applications require that both parties can obtain the final result. What's more, all of the previous PSI protocols are based on traditional PKI. In this paper, we first present the cryptographic primitive of identity-based symmetric private set intersection (IBSPSI) protocol and give the first construction. To get an efficient construction, we propose an identity-based proxyre-encryption scheme using combined public keys(IBR-CPK), which is the first identity-based proxy re-encryption without pairing. Our IBR-CPK scheme almost as efficient as traditional ECC. It is not only ensures the privacy of the information during the transmission process but also holds perfect additive homomorphism. The IBSPSI protocol that we construct based on IBR-CPK efficiently solves the unilateral PSI problem and simplifies the certificate management. We prove that our IBSPSI protocol achieves perfect privacy in the semi-honest model.

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

Private set intersection (PSI) is a specific application problem in the field of secure multi-party computation. It allows the participants to compute set intersection collaboratively without learning any additional information about the sets. It is a building block for many real-world privacy-related applications. The state-of-the-art PSI protocol is mainly based on Random Oblivious Transfer (ROT) and cuckoo hash. It is efficient enough in computation but needs high communication cost and high network bandwidth. In this paper, we describe a novel PSI protocol based on ROT and balance hash under semi-honest adversary model. Experiments demonstrate that our protocol has better performance than the cuckoo hash based PSI at the expense of leaking a part of the indexes of hash bucket. We have proved that the impact of information leakage on security is almost negligible. In addition, for the purpose of reducing the communication overhead, we utilize cuckoo filter to optimize our protocol.

Cloud computing paradigm builds on the foundations of distributed computing, grid computing, virtualisation, service orientation, etc. Cloud storage, which is one of the most attractive cloud services, offers numerous benefits from both the technology and functionality perspectives such as increased availability, flexibility, and functionality. Therefore, Cloud-based platforms have become fundamental to collaborative and data management systems. However, the new Cloud environment creates major collaborative and data management challenges such as efficient and reliable remote storage, online data query processing, data integrity, outsourcing computation, and secure virtualisation, among others. Such issues need to be carefully studied and solved to enable the wide deployment and adoption of Cloud computing in businesses, industry, etc. The purpose of this special issue is to publish recent advances in Cloud-based collaborative systems and Cloud storage. The special issue comprises five high-quality papers, which are arranged as follows. De Maio et al in the first paper1 “Social Media Marketing through Time-Aware Collaborative Filtering,” define a time-aware collaborative filtering for estimating users' interest along the time in Twitter. Their approach uses text analysis services to semantically annotate tweets' content and to track concepts considering post frequencies along the time. A model-based approach implementing K-Nearest Neighbors is used to estimate user's similarity representing their profile by sampling user's interest with three different techniques: Vectorial Representation, Symbolic Aggregation Approximation, and Median. The authors present experimental results comparing these techniques and performing model training in different time windows. The second paper2 “SERNOTATE: An Automated Approach for Business Service Description Annotation for efficient Service Retrieval and Composition” by Chotipant et al proposes an automated approach, namely, SERNOTATE, for business service description annotation for efficient service retrieval and composition. To that end, the authors employ new semantic-based linking approaches, namely, Extended Case-based Reasoning, Vector-based and Classification-based, that automatically annotate business services to relevant service concepts. The experimental results test and validate the applicability of the proposed approaches to the automatic annotation of business service descriptions to service concepts on a real-world dataset. In the third paper,3 “Sensor Data Management in the Cloud: Data Storage, Data Ingestion, and Data Retrieval,” the authors address an increasing need to capture, store, and analyse the dynamic semistructured data from sensors. A similar growth of semistructured data in the modern Web has led to the creation of NoSQL data stores for scalability, availability, and performance, whereas large-scale data processing frameworks for parallel analysis. The authors study how sensor data management can benefit from MongoDB with Apache Spark technologies, specifically for ingesting high-velocity sensor data and parallel retrieval of high volume data. The performance of MongoDB sharding and no-sharding databases with Apache Spark are evaluated to identify the right software environment for sensor data management. Park et al in the fourth paper,4 “Study on the SDN-IP based Solution of Well-known Bottleneck Problems in Private Sector of National R&E Network for Big Data Transfer,” deal with issues arising in Software Defined Networking (SDN). The key feature of SDN networks is the provisioning of dynamic service architecture. Therefore, it is more appropriate for the big data science computing trends, for handling today's big data science, which requires massive parallel processing and a constant demand for additional capacity and on demand connectivity. The authors bring the experience from KREONET (Korea Research Environment Open Network) and Korea National R&E Network, which adopt SDN as a platform service for big data scientific application. The big data application for KREONET SDN service comprises high-energy physics, astrophysics, biology/genetics, meteorology, artificial satellite data, etc. The last paper5 “Improved Outsourced Private Set Intersection Protocol Based on Polynomial Interpolation” by Yang et al investigates issues in private set intersection (PSI) protocols, which enable two parties to compute the intersection of their inputs without compromising anything about the datasets beyond the intersection. The authors present a variant of delegated private set intersection protocol secure in the semi-honest model under RSA assumption as well as an efficient and secure outsourcing computation algorithm for RSA crypto-system. Based on this algorithm, the authors transform a variant of delegated private set intersection protocol into an improved outsourced one, which enables the clients to perform only simple modular multiplication for computing during the execution of the protocol. Compared with the state of the art, the proposed protocol has a great advantage in efficiency. The editor of this special issue would like to thank all authors for their contributions and the reviewers for their constructive and useful feedback to authors. I would like to thank Prof Geoffrey Fox, the Editor-in-Chief of Concurrency and Computation: Practice and Experience, for the opportunity to edit this special issue and his support.

A private set intersection (PSI) protocol is a protocol to get the intersection of two sets, each of which belongs to one party, without disclosing extra information of each party’s set to the other party. In this paper, we propose a novel semi-honest PSI protocol without using any encryption primitive in a semi-honest security model. Specifically, we first slice all elements in the set and inject dummy slices. Then we utilize the greatest common divisor (GCD) to find the common parts of the two parties’ slice set product, which is the product of all slices. Finally, we filter the elements by utilizing the GCD to find out the intersection. Different from most previous PSI protocols, we get the intersection by calculation rather than comparison.Our protocol has many advantages over other state-of-the-art PSI protocols, such as robust security against quantum attacks, low communication cost, high computation efficiency when the bandwidth is low, etc. Through extensive experiments, we find the optimum parameters in our setting and demonstrate the performance of our protocol. Different from previous PSI protocols, the communication cost of our protocol varies with the cardinality of the intersection. In comparison, the communication cost of our protocol is the lowest, which gains an over \(200\%\) improvement than the communication-optimized PSI protocol spot-low (Pinkas et al., CRYPTO’19). In addition, the running time of our protocol is the lowest when the communication bandwidth is about 1 Mbps.KeywordsPSIGCDLow communication costs

Multi-party private set intersection (MP-PSI) allows m participants with private sets to calculate their intersection without revealing any information, which is quite different from the traditional two-party PSI. Although some efficient MP-PSI protocols already exist, the efficiency of these protocols decreases significantly in the scenarios with the increasing number of participants for the small sets setting. In this paper, we propose efficient MP-PSI protocols based on one-round two-party key agreement embedding with unconditional zero sharing technique and prove secure by resisting up to \(m-2\) parties collision in the semi-honest model and \(m-1\) parties collision in the malicious model. Compared with the current efficient MP-PSI, our protocol performs better in terms of the communication round, communication cost and the running time in small sets setting. Extensive experiments show that as the number of participants m increases from 5 to 50 and the upbound of the set size n scales from \(2^8\) to \(2^{10}\), the running time of our MP-PSI protocol is faster than those of the most efficient MP-PSI protocol [14] and the recent work [4]. Take the set size at \(2^{7}\) for example, as the number of the participants increases from 10 to 50, our protocol only requires 4–26\(\times \) less running time and 8–11\(\times \) less communication cost than those of [14], respectively. We conclude that our protocols are practical solutions suitable for the scenarios in small set setting with increasing participants.KeywordsPrivate set intersectionCollusion resistantLarge participantsSmall setsMalicious secure

Mixed-protocol multi-party computation framework towards complex computation tasks with malicious security

A private set intersection (PSI) protocol allows two parties to compute the intersection of their input sets privately. Most of the previous PSI protocols only output the result to one party and the other party gets nothing from running the protocols. However, a mutual PSI protocol in which both parties can get the output is highly desirable in many applications. A major obstacle in designing a mutual PSI protocol is how to ensure fairness. In this paper we present the first fair mutual PSI protocol which is efficient and secure. Fairness of the protocol is obtained in an optimistic fashion, i.e. by using an offline third party arbiter. In contrast to many optimistic protocols which require a fully trusted arbiter, in our protocol the arbiter is only required to be semi-trusted, in the sense that we consider it to be a potential threat to both parties’ privacy but believe it will follow the protocol. The arbiter can resolve disputes without knowing any private information belongs to the two parties. This feature is appealing for a PSI protocol in which privacy may be of ultimate importance.

National Statistical Offices (NSOs) collect extensive data on the international activities of firms within their borders. However, they typically lack information about the foreign partners with whom these firms trade. Linking import data from one NSO to corresponding export data from a partner NSO could significantly enhance statistics on firms’ international operations. While technically feasible, such linkage is legally constrained by strict privacy laws. Private set intersection (PSI) protocols may help address privacy concerns but require unique identifiers to avoid linkage errors. To overcome this limitation, we propose a PSI protocol with three innovations. First, we estimate the rates of linkage error by modeling the number of links from a given record. Second, we adjust an estimated population mean according to the estimated linkage accuracy. Lastly, our adjustment explicitly accounts for this accuracy without assuming a particular relationship among the target variables.

Over the last decade, pairing-based cryptography has found a wide range of interesting applications, both in cryptography and in computer/network security. It often yields the most elegant (if not always the most efficient) techniques. This talk overviews several topics to which pairing-based methods either have not been applied, or where they have not reached their potential. The first topic is “privacy-preserving set operations”, such as private set intersection (PSI) protocols. Despite lots of prior work, state-of-the-art (in terms of efficiency) PSI is grounded in more mundane non-pairing-based number theoretic settings. This is puzzling, since the same does not hold with closely related secret handshakes and affiliation-hiding key exchange (AH-AKE) techniques. The second topic is more applied: “security in unattended wireless sensor networks” (UWSNs). We discuss certain unique security issues occurring in UWSNs, overview some protection measures, and consider whether pairing-based cryptography has some applications in this context. The third topic is “privacy in mobile ad hoc networks” (MANETs). The central goal is to achieve privacy-preserving (i.e., tracking-resistant) mobility in the presence of malicious insiders, while maintaining security. Since security is based on authentication, which is, in turn, usually based on identities, routing and packet forwarding are very challenging. Pairing-based cryptography might offer some useful techniques for reconciling security and privacy in this context. Finally, we consider the topic of “secure code attestation for embedded devices” where the main challenge is: how an untrusted (and possibly compromised) device can convince a trusted verifier that it runs appropriate code. After discussing current approaches, once again, consider whether pairing techniques can be of use.

We describe a lightweight protocol for oblivious evaluation of a pseudorandom function (OPRF) in the presence of semihonest adversaries. In an OPRF protocol a receiver has an input r; the sender gets output s and the receiver gets output F(s; r), where F is a pseudorandom function and s is a random seed. Our protocol uses a novel adaptation of 1-out-of-2 OT-extension protocols, and is particularly efficient when used to generate a large batch of OPRF instances. The cost to realize m OPRF instances is roughly the cost to realize 3:5m instances of standard 1-out-of-2 OTs (using state-of-the-art OT extension). We explore in detail our protocol's application to semihonest secure private set intersection (PSI). The fastest state-of- the-art PSI protocol (Pinkas et al., Usenix 2015) is based on efficient OT extension. We observe that our OPRF can be used to remove their PSI protocol's dependence on the bit-length of the parties' items. We implemented both PSI protocol variants and found ours to be 3.1{3.6 faster than Pinkas et al. for PSI of 128-bit strings and sufficiently large sets. Concretely, ours requires only 3.8 seconds to securely compute the intersection of 220-size sets, regardless of the bitlength of the items. For very large sets, our protocol is only 4:3 slower than the insecure naive hashing approach for PSI.

Private set operations (PSO) address key challenges in cross‐organizational data sharing where multiple transport entities securely verify shared cargo manifests or delivery schedules without exposing sensitive information. Private set intersection (PSI) enables secure computation of common elements across private datasets, while its variant, PSI with cardinality (PSI‐CA), reveals only the intersection size. The PSO protocol based on Diffie‐Hellman (DH) key agreement has emerged as a prominent solution for communication‐sensitive applications. However, current research efforts mostly concentrate on two‐party implementations, which face inherent limitations in addressing the communication complexity when scaling to multiple participants. In this work, we firstly present E3PSI‐CA, a novel three‐party PSO protocol. This construction for the semi‐honest model leverages bilinear maps in DH key agreement and Bloom Filters (BF) for compact set representation, achieving remarkable efficiency. By integrating private information retrieval (PIR) techniques, the party in E3PSI‐CAcan efficiently compute the set intersection. To address the inherent accuracy limitations of the BF and extend security to the malicious model, we further propose an enhanced PSI scheme. This variant is built upon a “Ring” structure, within which we employ DH key agreement and OKVS data structure to achieve security against any single malicious party, while the OKVS simultaneously ensures perfect accuracy (zero false positives). We prove the security through the ideal/real simulation paradigm and conduct performance evaluations.

Private Set Intersection (PSI) enables two parties, each holding a private set to securely compute their intersection without revealing other information. This paper considers settings of secure statistical computations over PSI, where both parties hold sets containing identifiers with one of the parties having an additional positive integer value associated with each of the identifiers in her set. The main objective is to securely compute some desired statistics of the associated values for which its corresponding identifiers occur in the intersection of the two sets. This is achieved without revealing the identifiers of the set intersection. In this paper, we present protocols which enable the secure computations of statistical functions over PSI, which we collectively termed PSI-Stats. Implementations of our constructions are also carried out based on simulated datasets as well as on actual datasets in the business use cases that we defined, in order to demonstrate practicality of our solution. PSI-Stats incurs \(5\times \) less monetary cost compared to the current state-of-the-art circuit-based PSI approach due to Pinkas et al. (EUROCRYPT’19). Our solution is more tailored towards business applications where monetary cost is the primary consideration.KeywordsPrivate set intersectionHomomorphic encryptionStatistical functions

Nowadays, it is very popular to make friends, share photographs, and exchange news throughout social networks. Social networks widely expand the area of people’s social connections and make communication much smoother than ever before. In a social network, there are many social groups established based on common interests among persons, such as learning group, family group, and reading group. People often describe their profiles when registering as a user in a social network. Then social networks can organize these users into groups of friends according to their profiles. However, an important issue must be considered, namely many users’ sensitive profiles could have been leaked out during this process. Therefore, it is reasonable to design a privacy-preserving friends-finding protocol in social network. Toward this goal, we design a fuzzy interest matching protocol based on private set intersection. Concretely, two candidate users can first organize their profiles into sets, then use Bloom filters to generate new data structures, and finally find the intersection sets to decide whether being friends or not in the social network. The protocol is shown to be secure in the malicious model and can be useful for practical purposes.