Verifiable random functions (VRF), proposed in 1999, and selectively convertible undeniable signature (SCUS) schemes, proposed in 1990, are apparently thought as independent primitives in the literature. In this paper, we show that they are tightly related in the following sense: VRF is exactly SCUS; and the reverse also holds true under a condition. This directly yields several deterministic SCUS schemes based on existing VRF constructions. In addition, we create a new probabilistic SCUS scheme, which is very compact. We build efficient confirmation and disavowal protocols for the proposed SCUS schemes, based on what we call zero-knowledge protocols for generalized DDH and non-DDH. These zero-knowledge protocols are built either sequential, concurrent, or universally composable.

Kurosawa-Desmedt public-key encryption scheme is a variation of Cramer-Shoup public-key encryption schemes, which are the first practical schemes secure against adaptive chosen ciphertext attack (IND-CCA) in standard model. We introduce some variants of Kurosawa-Desmedt public-key encryption scheme which are also IND-CCA secure. Furthermore, the variants are either more efficient or less cryptographic assumptions than the original version.

In this paper, we explore privacy-preserving techniques in federated learning, including those can be used with both neural networks and decision trees. We begin by identifying how information can be leaked in federated learning, after which we present methods to address this issue by introducing two privacy-preserving frameworks that encompass many existing privacy-preserving federated learning (PPFL) systems. Through experiments with publicly available financial, medical, and Internet of Things datasets, we demonstrate the effectiveness of privacy-preserving federated learning and its potential to develop highly accurate, secure, and privacy-preserving machine learning systems in real-world scenarios. The findings highlight the importance of considering privacy in the design and implementation of federated learning systems and suggest that privacy-preserving techniques are essential in enabling the development of effective and practical machine learning systems.

We aim at constructing adaptive oblivious transfer protocols, enjoying fully simulatable security, from various well-known assumptions such as DDH, d-Linear, QR, and DCR. To this end, we present two generic constructions of adaptive OT, one of which utilizes verifiable shuffles together with threshold decryption schemes, while the other uses permutation networks together with what we call loosely-homomorphic key encapsulation schemes. The constructions follow a novel designing approach called “blind permutation”, which completely differs from existing ones. We then show that specific choices of the building blocks lead to concrete adaptive OT protocols with fully simulatable security in the standard model under the targeted assumptions. Our generic methods can be extended to build universally composable (UC) secure OT protocols, with a loss in efficiency.

Logistic regression is a powerful machine learning tool to classify data. When dealing with sensitive or private data, cares are necessary. In this paper, we propose a secure system for privacy-protecting both the training and predicting data in logistic regression via homomorphic encryption. Perhaps surprisingly, despite the non-polynomial tasks of training and predicting in logistic regression, we show that only additively homomorphic encryption is needed to build our system. Indeed, we instantiate our system with Paillier, LWE-based, and ring-LWE-based encryption schemes, highlighting the merits and demerits of each instantiation. Besides examining the costs of computation and communication, we carefully test our system over real datasets to demonstrate its utility.

Undeniable signature, and unpretendable signature schemes have been studied independently. In this paper, efficient schemes which serve as both at the same time are presented. The schemes find their typical application in anonymous auction where the winner cannot deny her bid; nobody can pretend to be the winner; and the anonymity of all losers is preserved. The security of the schemes is proved in the common reference string model under discrete logarithm type assumptions.

We present the concept of key-rotatable and security-updatable homomorphic encryption (KR-SU-HE) scheme, which is defined as a class of public-key homomorphic encryption in which the keys and the security of any ciphertext can be rotated and updated while still keeping the underlying plaintext intact and unrevealed. After formalising the syntax and security notions for KR-SU-HE schemes, we build a concrete scheme based on the Learning With Errors assumption. We then perform several careful implementations and optimizations to show that our proposed scheme is efficiently practical.

We build a privacy-preserving system of linear regression protecting both input data secrecy and output privacy. Our system achieves those goals simultaneously via a novel combination of homomorphic encryption and differential privacy dedicated to linear regression and its variants (ridge, LASSO). Our system is proved scalable over cloud servers, and its efficiency is extensively checked by careful experiments.

In this paper, we design and analyze some new and practical (selectively) convertible undeniable signature (SCUS) schemes in both random oracle and standard model, which enjoy several merits over existing schemes in the literature. In particular, we design the first practical RSA-based SCUS schemes secure in the standard model. On the path, we also introduce two moduli RSA assumptions, including the strong twin RSA assumption, which is the RSA symmetry of the strong twin Diffie-Hellman assumption (Eurocrypt'08).

We explicitly describe and analyse blind hierachical identity-based encryption (blind HIBE) schemes, which are natural generalizations of blind IBE schemes [20]. We then uses the blind HIBE schemes to construct: (1) An identity-based blind signature scheme secure in the standard model, under the computational Diffie-Hellman (CDH) assumption, and with much shorter signature size and lesser communication cost, compared to existing proposals. (2) A new mechanism supporting a user to buy digital information over the Internet without revealing what he/she has bought, while protecting the providers from cheating users.