arXiv subjects

Jinguang Han

Publications and source records attributed to Jinguang Han.

15 recordsLinked to original sources

arXiv2026

LTRAS: A Linkable Threshold Ring Adaptor Signature Scheme for Efficient and Private Cross-Chain Transactions

Despite the advantages of decentralization and immutability, blockchain technology faces significant scalability and throughput limitations, which has prompted the exploration of off-chain solutions like payment channels. Adaptor signatures have been considered a promising primitive for constructing such channels due to their support for atomicity, offering an alternative to traditional hash-timelock contracts. However, standard adaptor signatures may reveal signer identity, raising potential privacy concerns. While ring signatures can mitigate this issue by providing anonymity, they often introduce high communication overhead, particularly in multi-account payment settings commonly used in UTXO-based blockchains like Monero. To address these limitations, we propose a Linkable Threshold Ring Adaptor Signature (LTRAS) scheme, which integrates the conditional binding of adaptor signatures, the multi-account payment of threshold ring signatures, and the linkability for preventing double-spending. The formal definition, security model and concrete construction of LTRAS are provided. We also analyze its security and evaluate its performance through theoretical analysis and experimental implementation. Experimental results demonstrate that our scheme achieve significantly lower computation and communication overhead compared to existing schemes in large ring sizes and multi-account payment scenarios. Finally, we discuss its application in cross-chain atomic swaps, demonstrating its potential for enhancing privacy and efficiency in blockchain transactions.

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arXiv2025

Efficient Quantum-resistant Delegable Data Analysis Scheme with Revocation and Keyword Search in Mobile Cloud Computing

With the rapid growth of smart devices and mobile internet, large-scale data processing is becoming increasingly important, while mobile devices remain resource-constrained. Mobile Cloud Computing (MCC) addresses this limitation by offloading tasks to the cloud. Nevertheless, the widespread adoption of MCC also raises challenges such as data privacy, selective computation, efficient revocation, and keyword search. Additionally, the development of quantum computers also threatens data security in MCC. To address these challenges, we propose an efficient quantum-resistant delegable data analysis scheme with revocation and keyword search (EQDDA-RKS) for MCC. In the proposed scheme, an authorised mobile device can perform keyword searches and compute inner product values over encrypted data without disclosing any additional information. Meanwhile, if a user's function key is compromised, it can be revoked. To alleviate the burden on mobile devices, most of the computation which should be executed by the mobile device is outsourced to a cloud server, and the mobile device only needs to interact with a central authority once. Furthermore, an authorised mobile device can temporarily delegate its keyword search and function computation rights to a delegatee in case the device becomes unavailable due to power depletion, going offline, etc. Our scheme is formally proven secure in the standard model against quantum attacks, chosen plaintext attacks, chosen keyword attacks, and outside keyword guessing attacks. Furthermore, the analysis demonstrates that the number of interactions between a mobile device and the central authority is $O(1)$ in our scheme, rather than growing linearly with the number of functions, which is well-suited for MCC scenarios.

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arXiv2025

Privacy-Preserving Federated Learning from Partial Decryption Verifiable Threshold Multi-Client Functional Encryption

In federated learning, multiple parties can cooperate to train the model without directly exchanging their own private data, but the gradient leakage problem still threatens the privacy security and model integrity. Although the existing scheme uses threshold cryptography to mitigate the inference attack, it can not guarantee the verifiability of the aggregation results, making the system vulnerable to the threat of poisoning attack. We construct a partial decryption verifiable threshold multi client function encryption scheme, and apply it to Federated learning to implement the federated learning verifiable threshold security aggregation protocol (VTSAFL). VTSAFL empowers clients to verify aggregation results, concurrently minimizing both computational and communication overhead. The size of the functional key and partial decryption results of the scheme are constant, which provides efficiency guarantee for large-scale deployment. The experimental results on MNIST dataset show that vtsafl can achieve the same accuracy as the existing scheme, while reducing the total training time by more than 40%, and reducing the communication overhead by up to 50%. This efficiency is critical for overcoming the resource constraints inherent in Internet of Things (IoT) devices.

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arXiv2025

Flexible Threshold Multi-client Functional Encryption for Inner Product in Federated Learning

Federated learning (FL) is a distributed machine learning paradigm that enables multiple clients to collaboratively train a shared model without disclosing their local data. To address privacy issues of gradient, several privacy-preserving machine-learning schemes based on multi-client functional encryption (MCFE) have been proposed. However, existing MCFE-based schemes cannot support client dropout or flexible threshold selection, which are essential for practical FL. In this paper, we design a flexible threshold multi-client functional encryption for inner product (FTMCFE-IP) scheme, where multiple clients generate ciphertexts independently without any interaction. In the encryption phase, clients are able to choose a threshold flexibly without reinitializing the system. The decryption can be performed correctly when the number of online clients satisfies the threshold. An authorized user are allowed to compute the inner product of the vectors associated with his/her functional key and the ciphertext, respectively, but cannot learning anything else. Especially, the presented scheme supports clients drop out. Furthermore, we provide the definition and security model of our FTMCFE-IP scheme,and propose a concrete construction. The security of the designed scheme is formally proven. Finally, we implement and evaluate our FTMCFE-IP scheme.

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arXiv2025

Lattice-Based Dynamic $k$-Times Anonymous Authentication with Attribute-Based Credentials

With the development of Internet, privacy has become a primary concern of users. Anonymous authentication plays an important role in privacy-preserving systems. A $k$-times anonymous authentication ($k$-TAA) scheme allows a group member to anonymously authenticate to the application provider up to $k$ times. Considering quantum computing attacks, lattice-based $k$-TAA was introduced. However, existing schemes neither support the dynamic granting and revocation of users nor enable users to control the release of their attributes. In this paper, we construct the first lattice-based dynamic $k$-TAA, which offers limited times anonymous authentication, dynamic member management, attribute-based authentication, and post-quantum security. We present a concrete construction, and reduce its security to standard complexity assumptions. Notably, compared with existing lattice-based $k$-TAA, our scheme is efficient in terms of communication cost.

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arXiv2025

Inner-product Functional Encryption with Fine-grained Revocation for Flexible EHR Sharing

E-health record (EHR) contains a vast amount of continuously growing medical data and enables medical institutions to access patient health data conveniently.This provides opportunities for medical data mining which has important applications in identifying high-risk patients and improving disease diagnosis, etc.Since EHR contains sensitive patient information, how to protect patient privacy and enable mining on EHR data is important and challenging.Traditional public key encryption (PKE) can protect patient privacy, but cannot support flexible selective computation on encrypted EHR data.Functional encryption (FE) allows authorised users to compute function values of encrypted data without releasing other information, hence supporting selective computation on encrypted data. Nevertheless, existing FE schemes do not support fine-grained revocation and update, so they are unsuitable for EHR system. In this paper,we first propose an inner-product functional encryption with fine-grained revocation (IPFE-FR) scheme, and then apply it to a flexible EHR sharing system. Our scheme possesses the following features:(1) a group manager can revoke a specific function computation of medical institutions on encrypted EHR data,instead of all function computation rights. (2) a revoked medical institution is not allowed to compute the function value of encrypted EHR data not only generated after the revocation, but also generated before the revocation. (3) secret keys issued to the same medical institution are bound together to prevent collusion attacks. The formal definition and security model of the IPFE-FR scheme are proposed.Furthermore, we present a concrete construction and reduce its security to the Learning with Errors (LWE) assumption which is quantum-resistant. Finally, the theoretical analysis and experimental implementation of our scheme are conducted to show its efficiency.

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arXiv2025

VFEFL: Privacy-Preserving Federated Learning against Malicious Clients via Verifiable Functional Encryption

Federated learning is a promising distributed learning paradigm that enables collaborative model training without exposing local client data, thereby protecting data privacy. However, it also brings new threats and challenges. The advancement of model inversion attacks has rendered the plaintext transmission of local models insecure, while the distributed nature of federated learning makes it particularly vulnerable to attacks raised by malicious clients. To protect data privacy and prevent malicious client attacks, this paper proposes a privacy-preserving Federated Learning framework based on Verifiable Functional Encryption (VFEFL), without a non-colluding dual-server assumption or additional trusted third-party. Specifically, we propose a novel Cross-Ciphertext Decentralized Verifiable Functional Encryption (CC-DVFE) scheme that enables the verification of specific relationships over multi-dimensional ciphertexts. This scheme is formally treated, in terms of definition, security model and security proof. Furthermore, based on the proposed CC-DVFE scheme, we design a privacy-preserving federated learning framework that incorporates a novel robust aggregation rule to detect malicious clients, enabling the effective training of high-accuracy models under adversarial settings. Finally, we provide the formal analysis and empirical evaluation of VFEFL. The results demonstrate that our approach achieves the desired privacy protection, robustness, verifiability and fidelity, while eliminating the reliance on non-colluding dual-server assumption or trusted third parties required by most existing methods.

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arXiv2024

Multi-client Functional Encryption for Set Intersection with Non-monotonic Access Structures in Federated Learning

Federated learning (FL) based on cloud servers is a distributed machine learning framework that involves an aggregator and multiple clients, which allows multiple clients to collaborate in training a shared model without exchanging data. Considering the confidentiality of training data, several schemes employing functional encryption (FE) have been presented. However, existing schemes cannot express complex access control policies. In this paper, to realize more flexible and fine-grained access control, we propose a multi-client functional encryption scheme for set intersection with non-monotonic access structures (MCFE-SI-NAS), where multiple clients co-exist and encrypt independently without interaction. All ciphertexts are associated with an label, which can resist "mix-and-match" attacks. Aggregator can aggregate ciphertexts, but cannot know anything about the plaintexts. We first formalize the definition and security model for the MCFE-SI-NAS scheme and build a concrete construction based on asymmetric prime-order pairings. The security of our scheme is formally proven. Finally, we implement our MCFE-SI-NAS scheme and provide its efficiency analysis.

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arXiv2024

Privacy-Preserving Traceable Functional Encryption for Inner Product

Functional encryption introduces a new paradigm of public key encryption that decryption only reveals the function value of encrypted data. To curb key leakage issues and trace users in FE-IP, a new primitive called traceable functional encryption for inner product (TFE-IP) has been proposed. However, the privacy protection of user's identities has not been considered in the existing TFE-IP schemes. In order to balance privacy and accountability, we propose the concept of privacy-preserving traceable functional encryption for inner product (PPTFE-IP) and give a concrete construction. Our scheme provides the following features: (1) To prevent key sharing, a user's key is bound with both his/her identity and a vector; (2) The key generation center (KGC) and a user execute a two-party secure computing protocol to generate a key without the former knowing anything about the latter's identity; (3) Each user can verify the correctness of his/her key; (4) A user can calculate the inner product of the two vectors embedded in his/her key and in a ciphertext; (5) Only the tracer can trace the identity embedded in a key. The security of our scheme is formally reduced to well-known complexity assumptions, and the implementation is conducted to evaluate its efficiency. The novelty of our scheme is to protect users' privacy and provide traceability if required.

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arXiv2023

Blockchain-based Privacy-Preserving Public Key Searchable Encryption with Strong Traceability

Public key searchable encryption (PKSE) scheme allows data users to search over encrypted data. To identify illegal users, many traceable PKSE schemes have been proposed. However, existing schemes cannot trace the keywords which illegal users searched and protect users' privacy simultaneously. In some practical applications, tracing both illegal users' identities and the keywords which they searched is quite important to against the abuse of data. It is a challenge to bind users' identities and keywords while protecting their privacy. Moreover, existing traceable PKSE schemes do not consider the unforgeability and immutability of trapdoor query records, which can lead to the occurrence of frame-up and denying. In this paper, to solve these problems, we propose a blockchain-based privacy-preserving PKSE with strong traceability (BP3KSEST) scheme. Our scheme provides the following features: (1) authorized users can authenticate to trapdoor generation center and obtain trapdoors without releasing their identities and keywords; (2) when data users misbehave in the system, the trusted third party (TTP) can trace both their identities and the keywords which they searched; (3) trapdoor query records are unforgeable; (4) trapdoor query records are immutable because records are stored in blockchain. Notably, this scheme is suitable to the scenarios where privacy must be considered, e.g., electronic health record (EHR). We formalize both the definition and security model of our BP3KSEST scheme, and present a concrete construction. Furthermore, the security of the proposed scheme is formally proven. Finally, the implementation and evaluation are conducted to analyze its efficiency.

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arXiv2021

A Privacy-Preserving Logistics Information System with Traceability

Logistics Information System (LIS) is an interactive system that provides information for logistics managers to monitor and track logistics business. In recent years, with the rise of online shopping, LIS is becoming increasingly important. However, since the lack of effective protection of personal information, privacy protection issue has become the most problem concerned by users. Some data breach events in LIS released users' personal information, including address, phone number, transaction details, etc. In this paper, to protect users' privacy in LIS, a privacy-preserving LIS with traceability (PPLIST) is proposed by combining multi-signature with pseudonym. In our PPLIST scheme, to protect privacy, each user can generate and use different pseudonyms in different logistics services. The processing of one logistics is recorded and unforgeable. Additionally, if the logistics information is abnormal, a trace party can de-anonymize users, and find their real identities. Therefore, our PPLIST efficiently balances the relationship between privacy and traceability.

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arXiv2019

Oblivious Location-Based Service Query

Privacy-preserving location-base services (LBS) have been proposed to protect users' location privacy. However, there are still some problems in existing schemes: (1) a semi-trusted third party (TTP) is required; or (2) both the computation cost and communication cost to generate a query are linear in the size of the queried area. In this paper, to improve query efficiency, an oblivious location-based service query (OLBSQ) scheme is proposed. Our scheme captures the following features: (1) a semi-trusted TTP is not required; (2) a user can query services from a service provider without revealing her exact location; (3) the service provider can only know the size of a query made by a user; and (4) both the computation cost and the communication cost to generate a query is constant, instead of linear in the size of the queried area. We formalise the definition and security model of OLBSQ schemes. The security of our scheme is reduced to well-known complexity assumptions. The novelty is to reduce the computation cost and communication cost of making a query and enable the service provider to obliviously and incrementally generate decrypt keys for queried services. This contributes to the growing work of formalising privacy-preserving LBS schemes and improving query efficiency.

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arXiv2019

Anonymous Single Sign-on with Proxy Re-Verification

An anonymous Single Sign-On (ASSO) scheme allows users to access multiple services anonymously using one credential. We propose a new ASSO scheme, where users can access services anonymously through the use of anonymous credentials and unlinkably through the provision of designated verifiers. Notably, verifiers cannot link service requests of a user even if they collude. The novelty is that when a designated verifier is unavailable, a central authority can authorise new verifiers to authenticate the user on behalf of the original verifier. Furthermore, if required, a central verifier is authorised to deanonymise users and trace their service requests. We formalise the scheme along with a security proof and provide an empirical evaluation of its performance. This scheme can be applied to smart ticketing where minimising the collection of personal information of users is increasingly important to transport organisations due to privacy regulations such as General Data Protection Regulations (GDPR).

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arXiv2018

Privacy-Preserving Electronic Ticket Scheme with Attribute-based Credentials

Electronic tickets (e-tickets) are electronic versions of paper tickets, which enable users to access intended services and improve services' efficiency. However, privacy may be a concern of e-ticket users. In this paper, a privacy-preserving electronic ticket scheme with attribute-based credentials is proposed to protect users' privacy and facilitate ticketing based on a user's attributes. Our proposed scheme makes the following contributions: (1) users can buy different tickets from ticket sellers without releasing their exact attributes; (2) two tickets of the same user cannot be linked; (3) a ticket cannot be transferred to another user; (4) a ticket cannot be double spent; (5) the security of the proposed scheme is formally proven and reduced to well known (q-strong Diffie-Hellman) complexity assumption; (6) the scheme has been implemented and its performance empirically evaluated. To the best of our knowledge, our privacy-preserving attribute-based e-ticket scheme is the first one providing these five features. Application areas of our scheme include event or transport tickets where users must convince ticket sellers that their attributes (e.g. age, profession, location) satisfy the ticket price policies to buy discounted tickets. More generally, our scheme can be used in any system where access to services is only dependent on a user's attributes (or entitlements) but not their identities.

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arXiv2018

Anonymous Single-Sign-On for n designated services with traceability

Anonymous Single-Sign-On authentication schemes have been proposed to allow users to access a service protected by a verifier without revealing their identity which has become more important due to the introduction of strong privacy regulations. In this paper we describe a new approach whereby anonymous authentication to different verifiers is achieved via authorisation tags and pseudonyms. The particular innovation of our scheme is authentication can only occur between a user and its designated verifier for a service, and the verification cannot be performed by any other verifier. The benefit of this authentication approach is that it prevents information leakage of a user's service access information, even if the verifiers for these services collude which each other. Our scheme also supports a trusted third party who is authorised to de-anonymise the user and reveal her whole services access information if required. Furthermore, our scheme is lightweight because it does not rely on attribute or policy-based signature schemes to enable access to multiple services. The scheme's security model is given together with a security proof, an implementation and a performance evaluation.

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