Commitment schemes are cryptographic schemes that can be applied to zero-knowledge proof construction and blockchain construction. Recently, lattice-based cryptography has been intensively investigated due to the promising potential in quantum cryptography. Accordingly, commitment schemes based on lattice assumptions have been studied for practical applications. Notably, applications often require committing an arbitrary message with low communication costs, so commitment schemes must be satisfied with fewer length restrictions and fewer extensions to the messages. Several studies have been conducted to achieve the problem, including the study published by Baum et al. in 2018. However, the scheme in question still utilizes the message domain for extraneous purposes. We design a length-extension-free commitment scheme ComMWMin which the length of the message string is large relative to the length of the commitment string, improving on the commitment scheme of Baum et al. Furthermore, we prove that the hiding and binding properties of ComMWMare based on the hardness of the decisional search knapsack problem and extended search knapsack problems, respectively. Finally, we evaluate the computation costs of generating commitment value between ours and Baum et al.’s commitment scheme.
Vladimir Popov, Mikhail Krupin, Andrew Gross, Georgi Koreli
New advancements in zero-knowledge proof construction, including improvements in user experience, have made blockchain-based privacy applications more accessible than ever.However, additional measures are required to balance the needs of regulators, the basic privacy rights of users, and the constant threat of bad actors.To address these issues, privacy protocols can introduce features designed to increase transparency, encourage compliance, and prevent illicit use.In this paper, current privacy-preserving methods (privacy pools) are explained along with compliance measures designed to prevent illicit usage.These measures are divided into three broad categories: general restrictions, such as transaction limits, deposit quarantine, and geoblocking; selective disclosure, such as privacy-preserving KYC, proof of innocence, and opt-in reporting; and threat identification and prevention, including AML wallet screening.Each of these methods are described in detail along with examples of three privacypreserving protocols (Hinkal, RAILGUN, and zkBob) which utilize varying combinations of these methodologies to achieve privacy informed by selfregulatory compliance.
<abstract><p>The rapid development of blockchain transactions highlights the importance of privacy protection (including anonymity and confidentiality) and underscores the necessity for auditability. Some schemes, such as PGC and Miniledger, support privacy protection and auditability. However, they only offer incomplete privacy protection (i.e., supporting anonymity or confidentiality exclusively). In response to these issues, we propose a scheme that achieves partial anonymity, confidentiality, auditability, and traceability. By integrating a variant of Pedersen commitments and randomizable signatures, we achieve partial anonymity for users and the auditability of transactions, thereby protecting user privacy under audit conditions. Based on the twisted ElGamal encryption algorithm and specially constructed zero-knowledge proofs, we achieve confidentiality of transaction amounts under legal and regulatory conditions. System test results indicate that this scheme effectively meets the above requirements. The feasibility of this scheme is confirmed through system testing, comparative analysis, and security analysis.</p></abstract>
Ganga Rama Koteswara Rao, Hayder M. A. Ghanimi, V. S. Ramachandran, Dokhyl Al-Qahtani · 6 authors
A significant novel approach in distributed ML, Federated Learning (FL), enables multiple parties to work simultaneously on developing models while securing the confidentiality of their unique datasets. There are issues regarding privacy with FL, particularly for models that are being trained, because private information can be accessed from shared gradients or updates to the model. This investigation proposes SecureHE-Fed, a novel system that improves FL’s defense against attacks on privacy through the use of Homomorphic Encryption (HE) and Zero-Knowledge Proofs (ZKP). Before data from clients becomes involved in the learning procedure, SecureHE-Fed encrypts it. The following lets us determine encrypted messages without revealing the data as it is. As an additional security test, ZKP is employed to verify if modifications to models are valid without sharing the true nature of the information. By evaluating SecureHE-Fed with different FL techniques, researchers demonstrate that it enhances confidentiality while maintaining the precision of the model. The results of this work obtained validate SecureHE-Fed as a secure and scalable FL approach, and we recommend its use in applications where user confidentiality is essential.
Omar Ahmed, Charles Gouert, Nektarios Georgios Tsoutsos
Cloud computing has been a prominent technology that allows users to store their data and outsource intensive computations. However, users of cloud services are also concerned about protecting the confidentiality of their data against attacks that can leak sensitive information. Although traditional cryptography can be used to protect static data or data being transmitted over a network, it does not support processing of encrypted data. Homomorphic encryption can be used to allow processing directly on encrypted data, but a dishonest cloud provider can alter the computations performed, thus violating the integrity of the results. To overcome these issues, we propose PEEV (Parse, Encrypt, Execute, Verify), a framework that allows a developer with no background in cryptography to write programs operating on encrypted data, outsource computations to a remote server, and verify the correctness of the computations. The proposed framework relies on homomorphic encryption techniques as well as zero-knowledge proofs to achieve verifiable privacy-preserving computation. It supports practical deployments with low performance overheads and allows developers to express their encrypted programs in a high-level language, abstracting away the complexities of encryption and verification.
Federated Learning (FL) is a machine-learning approach enabling collaborative model training across multiple decentralized edge devices that hold local data samples, all without exchanging these samples. This collaborative process occurs under the supervision of a central server orchestrating the training or via a peer-to-peer network. The significance of FL is particularly pronounced in industries such as healthcare and finance, where data privacy holds paramount importance. However, training a model under the Federated learning setting brings forth several challenges, with one of the most prominent being the heterogeneity of data distribution among the edge devices. The data is typically non-independently and non-identically distributed (non-IID), thereby presenting challenges to model convergence. This report delves into the issues arising from non-IID and heterogeneous data and explores current algorithms designed to address these challenges.
The requirements for large amounts of data have promoted the rapid emergence of an industry for trading data. However, the current one-to-one trading constraints in the existing data trading schemes lead to low security and low efficiency. To tackle the challenges, a novel one-to-many distributed data trading scheme is proposed based on blockchain, which enables a data seller to sell one piece of data to multiple data buyers simultaneously, saving storage resources and computing resources significantly. Firstly, some new smart contracts are devised for two decentralized applications. Then, attribute-based searchable encryption technology is proposed to establish a data circulation scheme that realizes end-to-end encryption of data and ensures data security and highly efficient access. Finally, an inspection mechanism based on zero-knowledge proof and a pricing strategy based on the Stackelberg game are designed to guarantee fairness in trading and maximize revenue. The experiment results show that, in comparison to one-to-one trading, the high efficiency of this data trading scheme gradually emerges as the number of buyers (n) is greater than 2, and the run time is less than 1/10 of the former when n =35. Furthermore, the pricing strategy can enable buyers and sellers to obtain more revenue when$\text {n} \gt 4$.
Apurva K. Vangujar, Buvana Ganesh, Alia Umrani, Paolo Palmieri
This article presents a novel e-voting scheme that combines Group Identity-based Identification (GIBI) with Homomorphic Encryption (HE) based on the discrete logarithmic assumption. The proposed scheme uses the Schnorr-like GIBI scheme for voter identification and authorization using zero-knowledge proofs to ensure the anonymity and eligibility of voters. The voter$\textsf {v}_{i,j}$is granted the authorization to cast a valid vote for a single candidate$\textsf {C}_{k}$. The use of distributed ElGamal provides fairness while the use of partial shares for decryption enables individual and universal verifiability without the need for a central authority. The proposed scheme is secure under various scenarios and robust in the random oracle model. The GIBI-HE scheme offers a promising solution for e-voting, providing a sustainable and accessible environment for voters while supporting the unreusability of votes and protecting the privacy of voters.
Alberto Ballesteros-Rodríguez, Salvador Sánchez‐Alonso, Miguel‐Ángel Sicilia
The widespread integration of on-demand services founded on proprietary algorithms into various software applications has ushered into a new era of advanced service capabilities. However, using these services entails disclosing information by the customer, not only during the payment process but also when using the service, where certain personal information must be shared to obtain a more personalized service. This practice potentially exposes users to increased security risks in case of data security breaches. In this paper, we introduce a novel framework aimed at enhancing client privacy and ensuring service integrity within the context of computing services that rely on proprietary algorithms. A blockchain-based approach is proposed to enhance user privacy throughout service provision, encompassing both the payment process and the verification of the provided service. Our proposal leverages properties of distributed ledger networks to improve user privacy during payment transactions and incorporates a verification system using zero-knowledge proofs on blockchain to validate the integrity of the contracted service. Finally, we analyze the privacy, overhead, and performance aspects of the framework, employing custom proprietary algorithms. We illustrate this through examples of Convolutional Neural Networks with multiple layers, undisclosed to the client. This emphasizes the potential benefits of its applicability for both service providers and clients.
Electronic healthcare (E-health) cloud system enables electronic health records (EHRs) sharing and improves efficiency of diagnosis and treatment. In order to address EHRs confidentiality and authorized user access control in E-health cloud, attribute-based proxy re-encryption (ABPRE) has been widely employed which provides dynamic fine-grained access control over encrypted EHRs. Unfortunately, existing ABPRE schemes still have the following defects: 1) capacity of attribute-universe is defined at setup; 2) verifiable mechanism for re-encryption reveals EHRs about patients; 3) traditional access policy reveals sensitive information pertaining to patients. This paper focuses on these issues and presents large-universe, verifiable and privacy-preserving dynamic fine-grained access control scheme for E-health cloud. More details, we solve limitation of attribute-universe to large-universe, which means that attributes aren’t required to be enumerated at setup. Considering disclosure of underlying EHRs in verifiable mechanism, scheme introduces non-interactive zero-knowledge proof as verifiable mechanism that supports public validation and doesn’t leak EHRs of patients. Furthermore, partially hidden policy is employed to protect privacy of patients in policy, which divides attribute into attribute name and attribute value, displaying attribute name and hiding attribute value. Finally, experimental evaluation is given that demonstrates the more comprehensive functionality of our scheme without sacrificing significant computational overhead.
Engin Zeydan, Luis Blanco, Josep Mangues‐Bafalluy, Şuayb S. Arslan · 7 authors
Blockchain network (BCN)-based Self-Sovereign Identity (SSI) has emerged lately as an identity and access management framework that is based on Distributed Ledger Technology (DLT) and allows users to control their own data. Federated Learning (FL), on the other hand, provides a collaborative framework to update Machine Learning (ML) models without relying explicitly on data exchange between the users. This paper investigates identity management and authentication for vehicle users in the context of FL. We propose a novel approach based on blockchain-based SSI, which focuses on maintaining the authenticity and integrity of vehicle users’ identities and data exchanged between the users and the aggregation server during the execution of the FL iterations. A primary objective of this paper is to achieve shorter durations for credential operations in an FL setting as the system size scales out. Integrating BCN-based SSI into the FL framework addresses several critical FL challenges, ensuring enhanced system security and operational integrity. This synergy of BCN-based SSI with federated learning enables robust identity verification providing a solution to fundamental trustworthiness issues in FL without sacrificing the benefits of decentralized data control, improving both the performance and reliability of the FL system. Experimental results suggest that the proposed FL-based system, together with credential management on a blockchain platform, has the potential to significantly improve data integrity and ensure the authentication of users. More specifically, the results of the FL system demonstrate that it takes longer (on the order of a hundred seconds) as the number of rounds and clients increase, while the implemented Decentralized Identifier (DID) system relying on BCN-based SSI has dramatically shorter dedicated time for completing credential operations.