Federated learning is a method where multiple participants collaboratively train a model using machine learning techniques, such as deep learning, while keeping each participant's data private through the use of a central server. However, there are cases in which participant input information is leaked to the server. To solve this problem, several protocols have been proposed. In particular, Phong et al. (in IEEE TIFS 2018) proposed a protocol that protects participant information against a server using homomorphic encryption. Later, Park et al. (in ICTC 2022) proposed an improved protocol, where each client has a different secret-key for the homomorphic encryption. In this paper, we show that the Park et al.'s protocol has some weakness and propose the secure one based on the previously proposed protocols.

One of the most important processes in big data analysis is outlier detection, where anomalies of observed data are detected and eliminated to improve the system performance. Many approaches to outlier detection have been proposed so far for classification and prediction purposes. This paper focus on the outlier detection under a practical circumstance such that multiple organizations possess different data sets of a specific task, while they cannot directly share with each other from a privacy point of view; that is, each organization is not allowed to provide their sensitive data to other entities but they are keen to cooperate in data analysis for some reasons. To address this issue, we present a new outlier detection approach to the data analysis for multiple organizations using a decision tree ensemble based on the so-called federated learning scheme. In this paper, we extend an existing outlier detection mode called Isolation Forest to the federated learning concept so that data protection of each entity can be achieved by introducing an additive homomorphic encryption. The experimental results for several benchmark data sets demonstrate that the proposed privacy-preserving Isolation Forest (pp-iForest) achieves stable classification performance, which is almost the same as that under a single-organization setting, even when the number of organization is increased.

Summary Based on cloud servers' powerful storage and computing resources, users can store mass encrypted data in the cloud and outsource complex encryption computations to the cloud servers. Since cloud servers cannot be completely trusted, then data privacy and integrity are concerned about hot issues. We focus on the following problems in outsourced encryptions: how to protect the data privacy and how to check the integrity of data and the correctness of cloud server's outsourcing computations. In this paper, we at first propose a verifiable chaotic encryption based on Chebyshev polynomials. The scheme supports verifiable function for data integrity. To further improve the efficiency of the scheme, a corresponding outsourced encryption scheme is constructed, where the heavy overhead evaluations of Chebyshev polynomials are transferred from the user side to the cloud server. The outsourced encryption also provides the checkability for data integrity and correctness of cloud computations. The scheme is suitable for mobile users with limited computing resources. Moreover, the newly proposed scheme no longer depends upon the simple heuristic analysis. It achieves the indistinguishability under chosen‐ciphertext attacks (IND‐CCA) in the standard model based on the Chebyshev‐based Decisional Diffie‐Hellman (CDDH) assumption. Thus, we answer a long‐term open problem for building a chaotic encryption scheme with provable security in the sense of the IND‐CCA.

We present a privacy-preserving deep learning system in which many learning participants perform neural network-based deep learning over a combined dataset of all, without revealing the participants' local data to a central server. To that end, we revisit the previous work by Shokri and Shmatikov (ACM CCS 2015) and show that, with their method, local data information may be leaked to an honest-but-curious server. We then fix that problem by building an enhanced system with the following properties: 1) no information is leaked to the server and 2) accuracy is kept intact, compared with that of the ordinary deep learning system also over the combined dataset. Our system bridges deep learning and cryptography: we utilize asynchronous stochastic gradient descent as applied to neural networks, in combination with additively homomorphic encryption. We show that our usage of encryption adds tolerable overhead to the ordinary deep learning system.

Logistic regression is a powerful machine learning tool to classify data. When dealing with sensitive data such as private or medical information, cares are necessary. In this paper, we propose a secure system for protecting the training data in logistic regression via homomorphic encryption. Perhaps surprisingly, despite the non-polynomial tasks of training in logistic regression, we show that only additively homomorphic encryption is needed to build our system. Our system is secure and scalable with the dataset size.

PRINCESS (Proxy Re-encryption with INd-Cca security in an Encrypted file Storage System) is a secure storage system which utilizes special proxy re-encryption technology. With PRINCESS, the files encrypted in accordance with the confidentiality levels can be shared among appointed users while remaining encrypted. In this poster/demo, we show the efficiency of PRINCESS, which can be applied to a Body Area Network information sharing, automobile information sharing, etc. This system facilitates the potential for new services that require privacy data to be shared securely via cloud technology.

In threshold public-key encryption (TPKE), the decryption key is divided into n shares, each one of which is given to a different decryption user in order to avoid single points of failure. A robust TPKE is that if threshold decryption of a valid ciphertext fails, the combiner can identify the decryption users that supplied invalid partial decryption shares. In this paper, we propose a practical and efficient TPKE scheme which is robust and non-interactive. Security against chosen-ciphertext attacks (CCAs) can be proved in the standard model under the hashed Diffie–Hellman assumption in bilinear groups. The security reduction is tight and simple. We use an instantiation hash function of the Kiltz's key encapsulation mechanism and Lai et al.'s chosen-ciphertext secure technique to construct a TPKE scheme. Moreover, our scheme is more simple and shown to be more efficient than currently existing CCA-secure TPKE schemes.

In this paper, we build a family of chameleon hash functions and strongly unforgeable one-time signature schemes based on the intractability assumption of the discrete logarithm problem (DLP) over inner automorphism groups. Since the DLP assumption over inner automorphism groups does not admit sub-exponential attacks, thus the sizes of the working parameters used in our constructions are shorten significantly. This leads to remarkable gains for our proposals both in running time and in storage space. In addition, as far as we know, this is the first time to build CHF and OTS based on noncommutative groups.

ABSTRACT We propose a new public‐key cryptosystem named conjugacy search problem‐based Diffie–Hellman integrated encryption scheme (CSP‐DHIES), by using conjugation‐related assumptions for a special monoid of matrices of truncated multi‐variable polynomials over the ring ℤ12where the CSP is assumed to be intractable. Our construction can be viewed as the first noncommunicative variant of the well‐known DHIES cryptosystem. Under the assumptions of the intractability of the CSP‐based hash Diffie–Hellman problem and the CSP‐based oracle Diffie–Hellman problem, our scheme is provably secure against both chosen‐plaintext attacks and secure against chosen‐ciphertext attacks. Our proofs are constructed in the standard model. We also discuss the possibility of implementing our proposal using braid groups. Copyright © 2011 John Wiley & Sons, Ltd.

The notion of a splitting authentication code is very important in the context of an authentication code with arbitration. Ogata et al. [Discrete Math., 279 (2004), pp. 383--405] characterized an optimal splitting authentication code in terms of a splitting balanced incomplete block design (BIBD). A $(v,u \times c,1)$-splitting BIBD is a pair $({\cal V}, {\cal B})$, where ${\cal V}$ is a v-set of points and ${\cal B}$ is a collection of $u \times c$ arrays, called blocks, with entries from ${\cal V}$, such that any point of ${\cal V}$ can occur at most once in any block, and forany two distinct points x and y of ${\cal V}$, there is exactly one block of ${\cal B}$ in which x and y occur in different rows. In this paper, we describe various combinatorial constructions for splitting BIBDs (or, equivalently, optimal splitting authentication codes). We show that the necessary conditions for the existence of a $(v,u \times c,1)$-splitting BIBD (or, equivalently, an optimal c-splitting authentication code with u source states and v messages) are also sufficient for (u,c) = (2,2t) for any positive integer t,(u,c) = (2,3) with a definite exception of v = 10,(u,c) = (3,2) with a definite exception of v =9, and (u,c) = (4,2) with two possible exceptions of v = 49,385.