Fuzzy PSI is a variant of PSI, which on input a set of points from the receiver and sender respectively, allows the receiver to learn which of the sender's points lie within a threshold distance δ under a specific distance metric.

Private Set Intersection (PSI) enables parties to compute the intersection of their input sets while preserving privacy. While most PSI protocols are designed for balanced scenarios with sets of similar sizes, unbalanced PSI addresses situations where a server with a large database (e.g., millions of records) performs PSI with multiple clients, each with a set of only a few hundred elements. In this scenario, it is desirable for the server's computation on its large set to be performed offline and reusable, which we refer to as the "Client-Independent" property. However, existing offline/online unbalanced PSI protocols rely on less efficient OPRF constructions, which involve either computationally expensive exponential operations or communication-intensive garbled circuits. In this work, we present a framework for offline/online unbalanced PSI, with its core component being a novel functionality called "Client-Independent Relaxed OPRF" (ci-rOPRF). The key insight behind ci-rOPRF is to capture the requirements for OPRF in offline/online scenarios. To realize this functionality, we propose two constructions of ci-rOPRF, inspired by the top-performing CM-OPRF (CRYPTO '20) and VOLE-OPRF (EUROCRYPT '21), respectively. Leveraging these efficient ci-rOPRF constructions, we design highly efficient offline/online unbalanced PSI protocols. Furthermore, we extend this framework with two enhancements: one supports set updates, while the other reduces offline communication costs. Our C++ implementation demonstrates highly efficient performance. For instance, in the online phase, our fastest unbalanced PSI protocol computes the intersection of a client set with 2^12 elements and a server set with 2^28 elements in just 0.55 seconds and 0.62 MiB of communication on a 100 Mbps WiFi connection. Comparisons with state-of-the-art unbalanced PSI protocols show that our protocols significantly outperform existing solutions in the semi-honest model on most metrics.

In recent years, the medical industry is generating a large amount of data. How to securely store and reliably share these medical data has been a hot research topic. Cloud storage technology can be applied to the medical industry to adapt to the rapid growth of medical data. However, cloud-based data storage and sharing systems face a series of security issues: whether the integrity of outsourced medical data can be guaranteed, and malicious access between different medical institutions may leak user's privacy. This article proposes a system that simultaneously solves the integrity auditing of medical data and securely data sharing between different medical institutions under the terminal-edge-cloud framework. Specifically, patients/doctors are treated as terminal users, medical institutions are viewed as edge nodes, and medical clouds form the central storage layer. In the process of data auditing, third-party auditor can achieve integrity auditing of medical cloud storage data. Moreover, different medical institutions use private-set-intersection technology to share the common user's electronic medical data, while for other users not in intersection set, their data does not need to be shared. Finally, security and performance analyses show that our proposed system is provable secure and has high computational and communication efficiency.

We present an efficient and simple multi-party private set intersection cardinality (PSI-CA) protocol that allows several parties to learn the intersection size of their private sets without revealing any other information. Our protocol is highly efficient because it only utilizes the Oblivious Key-Value Store and zero-sharing techniques, without incorporating components such as OPPRF (Oblivious Programmable Pseudorandom Function) which is the main building block of multi-party PSI-CA protocol by Gao et al. (PoPETs 2024). Our protocol exhibits better communication and computational overhead than the state-ofthe-art. To compute the intersection between 16 parties with a set size of 220each, our PSI-CA protocol only takes 5.84 seconds and 326.6 MiB of total communication, which yields a reduction in communication by a factor of up to 2.4× compared to the state-of-the-art multi-party PSI-CA protocol of Gao et al. (PoPETs 2024). We prove that our protocol is secure in the presence of a semi-honest adversary who may passively corrupt any (t−2)-out-of-t parties once two specific participants are non-colluding.

Abstract Signcryption originally proposed by Zheng (CRYPTO′97) is a useful cryptographic primitive that provides strong confidentiality and integrity guarantees. This article addresses the question whether it is possible to homomorphically compute arbitrary functions on signcrypted data. The answer is affirmative and a new cryptographic primitive, homomorphic signcryption (HSC) with public plaintext‐result checkability is proposed that allows both to evaluate arbitrary functions over signcrypted data and makes it possible for anyone to publicly test whether a given ciphertext is the signcryption of the message under the key. Two notions of message privacy are also investigated: weak message privacy and message privacy depending on whether the original signcryptions used in the evaluation are disclosed or not. More precisely, the contributions are two‐fold: (i) two different definitions of HSC with public plaintext‐result checkability is provided for arbitrary functions in terms of syntax, unforgeability and message privacy depending on if the homomorphic computation is performed in a private or in a public evaluation setting, (ii) two HSC constructions are proposed: one for a public evaluation setting and another for a private evaluation setting and security is formally proved.

With the rapid development of the Internet of Things (IoT) a lot of critical information is shared however without having guarantees about the origin and integrity of the information. Digital signatures can provide important integrity guarantees to prevent illegal users from getting access to private and sensitive data in various IoT applications. Functional signatures, introduced by Boyle, Goldwasser and Ivan (PKC 2014) as signatures with a finegrained access control, allow an authority to generate signing keys corresponding to various functions such that a user with a signing key for a function f , can sign the image of the function f on a message m i.e., can sign f ( m ). Okamoto and Takashima (PKC 2013) firstly proposed the notion of a decentralized multi-authority functional signature (DMA-FS) scheme, which supports non-monotone access structures combined with inner-product relations. In this paper, we generalise the definition of DMA-FS proposed by Okamoto et al. (PKC13) for even more general policy functions, which support any polynomial-size boolean predicates other than the inner product relation and allow modifications of the original message. In our multi-authority functional signature (MAFS), there are multiple authorities and each one is able to certify a specific function and issue a corresponding functional signing key for each individual with some property, rendering them very useful in application settings such smart homes, smart cities, smart health care etc. We also provide a general transformation from a standard signature scheme to a MAFS scheme. Moreover, we present a way to build a function private MAFS from a FS without function privacy together with SNARKs.