Optimally Efficient Multi-party Fair Exchange and Fair Secure Multi-party Computation

Multi-party fair exchange (MFE) and fair secure multi-party computation (fair SMPC) are under-studied fields of research, with practical importance. In particular, we consider MFE scenarios where at the end of the protocol, either every participant receives every other participant’s item, or no participant receives anything. We analyze the case where a trusted third party (TTP) is optimistically available, although we emphasize that the trust put on the TTP is only regarding the fairness , and our protocols preserve the privacy of the exchanged items against the TTP. In the fair SMPC case, we prove that a malicious TTP can only harm fairness, but not security . We construct an asymptotically optimal multi-party fair exchange protocol that requires a constant number of rounds (in comparison to linear) and O(n 2 ) messages (in comparison to cubic), where n is the number of participating parties. In our protocol, we enable the parties to efficiently exchange any item that can be efficiently put into a verifiable encryption (e.g., signatures on a contract). We show how to apply this protocol on top of any SMPC protocol to achieve fairness with very little overhead (independent of the circuit size). We then generalize our protocol to efficiently handle any exchange topology (participants exchange items with arbitrary other participants). Our protocol guarantees fairness in its strongest sense: even if all n-1 other participants are malicious and colluding with each other, the fairness is still guaranteed.

Enhancing Privacy in PUF-Cash through Multiple Trusted Third Parties and Reinforcement Learning

Electronic cash ( e-Cash ) is a digital alternative to physical currency such as coins and bank notes. Suitably constructed, e-Cash has the ability to offer an anonymous offline experience much akin to cash, and in direct contrast to traditional forms of payment such as credit and debit cards. Implementing security and privacy within e-Cash, i.e., preserving user anonymity while preventing counterfeiting, fraud, and double spending, is a non-trivial challenge. In this article, we propose major improvements to an e-Cash protocol, termed PUF-Cash, based on physical unclonable functions ( PUFs ). PUF-Cash was created as an offline-first, secure e-Cash scheme that preserved user anonymity in payments. In addition, PUF-Cash supports remote payments; an improvement over traditional currency. In this work, a novel multi-trusted-third-party exchange scheme is introduced, which is responsible for “blinding” Alice’s e-Cash tokens; a feature at the heart of preserving her anonymity. The exchange operations are governed by machine learning techniques which are uniquely applied to optimize user privacy, while remaining resistant to identity-revealing attacks by adversaries and trusted authorities. Federation of the single trusted third party into multiple entities distributes the workload, thereby improving performance and resiliency within the e-Cash system architecture. Experimental results indicate that improvements to PUF-Cash enhance user privacy and scalability.

Federated Trusted Third Party as an Approach for Privacy Preserving Record Linkage in a Large Network of University Medicines in Pandemic Research

BackgroundThe Federal Ministry of Research and Education funded the Network of University Medicine for establishing an infrastructure for pandemic research. This includes the development of a COVID-19 Data Exchange Platform (CODEX) that provides standardised and harmonised data sets for COVID-19 research. Nearly all university hospitals in Germany are part of the project and transmit medical data from the local data integration centres to the CODEX platform. The medical data on a person that has been collected at several sites is to be made available on the CODEX platform in a merged form. To enable this, a federated trusted third party (fTTP) will be established, which will allow the pseudonymised merging of the medical data. The fTTP implements privacy preserving record linkage based on Bloom filters and assigns pseudonyms to enable re-pseudonymisation during data transfer to the CODEX platform.ResultsThe fTTP was implemented conceptually and technically. For this purpose, the processes that are necessary for data delivery were modelled. The resulting communication relationships were identified and corresponding interfaces were specified. These were developed according to the specifications in FHIR and validated with the help of external partners. Existing tools such as the identity management system E-PIX® were further developed accordingly so that sites can generate Bloom filters based on person identifying information. An extension for the comparison of Bloom filters was implemented for the federated trust third party. The correct implementation was shown in the form of a demonstrator and the connection of two data integration centres.ConclusionsThis article describes how the fTTP was modelled and implemented. In a first expansion stage, the fTTP was exemplarily connected through two sites and its functionality was demonstrated. Further expansion stages, which are already planned, have been technically specified and will be implemented in the future in order to also handle cases in which the privacy preserving record linkage achieves ambiguous results. The first expansion stage of the fTTP is available in the University Medicine network and will be connected by all participating sites in the ongoing test phase.

Publicly Verifiable M + 1st-Price Auction Fit for IoT with Minimum Storage

In an M + 1 st-price auction, all bidders submit their bids simultaneously, and the M highest bidders purchase M identical goods at the M + 1 st bidding price. Previous research is constructed based on trusted managers such as a trusted third party (TTP), trusted mix servers, and honest managers. All of the previous auctions are not fit for edge-assisted IoT since they need TTP. In this paper, we formalize a notion of commutative bi-homomorphic multiparty encryption and achieve no-TTP M + 1 -st auction based on blockchain with public verifiability. Our M + 1 st auction guarantees financial fairness, robustness, and correctness without TTP and is secure under a malicious model for the first time. Our M + 1 st auction can be executed over a distributed network and is thus fit for edge-assisted IoT. Furthermore, our formalized commutative bi-homomorphic multiparty encryption can be used in various applications for edge-assisted IoT, which needs to protect privacy and correctness.

A Quantum Dual-Signature Protocol Based on SNOP States without Trusted Participant

Quantum dual-signature means that two signed quantum messages are combined and expected to be sent to two different recipients. A quantum signature requires the cooperation of two verifiers to complete the whole verification process. As an important quantum signature aspect, the trusted third party is introduced to the current protocols, which affects the practicability of the quantum signature protocols. In this paper, we propose a quantum dual-signature protocol without arbitrator and entanglement for the first time. In the proposed protocol, two independent verifiers are introduced, here they may be dishonest but not collaborate. Furthermore, strongly nonlocal orthogonal product states are used to preserve the protocol security, i.e., no one can deny or forge a valid signature, even though some of them conspired. Compared with existing quantum signature protocols, this protocol does not require a trusted third party and entanglement resources.