Withdrawable Signature: How to Call off a Signature

Digital signatures are a cornerstone of security and trust in cryptography, providing authenticity, integrity, and non-repudiation. Despite their benefits, traditional digital signature schemes suffer from inherent immutability, offering no provision for a signer to retract a previously issued signature. This paper introduces the concept of a withdrawable signature scheme, which allows for the retraction of a signature without revealing the signer\u27s private key or compromising the security of other signatures the signer created before. This property, defined as ``withdrawability\u27\u27, is particularly relevant in decentralized systems, such as e-voting, blockchain-based smart contracts, and escrow services, where signers may wish to revoke or alter their commitment. The core idea of our construction of a withdrawable signature scheme is to ensure that the parties with a withdrawable signature are not convinced whether the signer signed a specific message. This ability to generate a signature while preventing validity from being verified is a fundamental requirement of our scheme, epitomizing the property of withdrawability. After formally defining security notions for withdrawable signatures, we present two constructions of the scheme based on the pairing and the discrete logarithm. We provide proofs that both constructions are unforgeable under insider corruption and satisfy the criteria of withdrawability. We anticipate our new type of signature will significantly enhance flexibility and security in digital transactions and communications

A Value-based Approach to Co-designing Symbiotic Product-service system

Abstract For a sustainable service system, the symbiosis of stakeholders is one of the critical factors. In that the symbiotic relation between stakeholders can be sustained based on the mutual benefit, exchanging value in a reciprocal way is significant. However, it is a challenge to generate symbiotic solution producing mutual value in service system design due to the complexity of the network, involving the different interests of stakeholders. This study motivated from the new perspective on value exchange in terms of Product Service System and developed the Value based co-design model (VCM). It is the methodological model for generating symbiotic solution through value exchange between stakeholders with new perspective on the resource. The model is applied to the PSS workshop for promoting sustainable food production and consumption. Finally, the insights about the model in terms of generating symbiotic solution and the designers' role in this specific model are discussed

Online/Offline Identity-Based Signcryption Revisited

In this paper, we redefine a cryptographic notion called Online/Offline Identity-Based Signcryption. It is an ``online/offline\u27\u27 version of identity-based signcryption, where most of the computations are carried out offline while the online part does not require any heavy computations such as pairings or multiplications on elliptic curve. It is particularly suitable for power-constrained devices such as smart cards. We give a concrete implementation of online/offline identity-based signcryption, which is very efficient and flexible. Unlike all the previous schemes in the literature, our scheme does not require the knowledge of receiver\u27s information (either public key or identity) in the offline stage. The receiver\u27s identity and the message to be signcrypted are only needed in the online stage. This feature provides a great flexibility to our scheme and makes it practical to use in real-world applications. To our knowledge, our scheme is the first one in the literature to provide this kind of feature. We prove that the proposed scheme meets strong security requirements in the random oracle model, assuming the Strong Diffie-Hellman (SDH) and Bilinear Diffie-Hellman Inversion (BDHI) are computationally hard

Dumbo: Faster Asynchronous BFT Protocols

HoneyBadgerBFT, proposed by Miller et al. [32] as the first practical asynchronous atomic broadcast protocol, demonstrated impressive performance. The core of HoneyBadgerBFT (HB-BFT) is to achieve batching consensus using asynchronous common subset protocol (ACS) of Ben-Or et al., constituted with $n$ reliable broadcast protocol (RBC) to have each node propose its input, followed by $n$ asynchronous binary agreement protocol (ABA) to make a decision for each proposed value ($n$ is the total number of nodes). In this paper, we propose two new atomic broadcast protocols (called Dumbo1, Dumbo2) both of which have asymptotically and practically better efficiency. In particular, the ACS of Dumbo1 only runs a small $k$ (independent of $n$) instances of ABA, while that of Dumbo2 further reduces it to constant! At the core of our techniques are two major observations: (1) reducing the number of ABA instances significantly improves efficiency; and (2) using multi-valued validated Byzantine agreement (MVBA) which was considered sub-optimal for ACS in [32] in a more careful way could actually lead to a much more efficient ACS. We implement both Dumbo1, Dumbo2 and deploy them as well as HB-BFT on 100 Amazon EC2 t2.medium instances uniformly distributed throughout 10 different regions across the globe, and run extensive experiments in the same environments. The experimental results show that our protocols achieve multi-fold improvements over HoneyBadgerBFT on both latency and throughput, especially when the system scale becomes moderately large

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Identity-Based Threshold Signature Scheme from the Bilinear Pairings (Extended Abstract)

The focus of this paper is to formalize the concept of identity-based threshold signature and give the first provably secure scheme based on the bilinear pairings. An important feature of our scheme is that a private key associated with an identity rather than a master key of the Public Key Generator is shared among signature generation servers, which is more desirable in practice. Another interesting aspect of our results is that the security of one of the verifiable secret sharing schemes used to construct the identity-based threshold signature scheme is relative to a slightly modified version of the Generalized Tate Inversion problem recently proposed by Joux