Rakyong Choi

Homomorphic Encryption (HE) makes it possible to compute on encrypted data without decryption. In lattice-based HE, a ciphertext contains noise, which accumulates along with homomorphic computations. Bootstrapping refreshes the noise and it is possible to perform arbitrary-depth computations on HE with bootstrapping, which we call Fully Homomorphic Encryption (FHE). In this article, we propose a new general bootstrapping technique for RLWE-based schemes and its practical instantiation for FHE. It can be applied to all three RLWE-based leveled FHE schemes: Brakerski-Gentry-Vaikuntanathan (BGV), Brakerski/Fan-Vercauteren (BFV), and Cheon-Kim-Kim-Song (CKKS) with minor deviations in the algorithms. Our new construction of bootstrapping extracts a noiseless ciphertext for a part of the input, scales it, and finally removes it. In contrast with previous bootstrapping algorithms, the proposed method consumes only 1–2 levels and uses smaller parameters. For BGV and BFV, our new bootstrapping does not have any restrictions on a plaintext modulus unlike typical cases of the previous methods. The error introduced by our approach for CKKS is comparable to a rescaling error, allowing us to preserve a large amount of precision after bootstrapping.

The exponential growth of big data and deep learning has increased the data exchange traffic in society. Machine Learning as a Service, (MLaaS) which leverages deep learning techniques for predictive analytics to enhance decision-making, has become a hot commodity. However, the adoption of MLaaS introduces data privacy challenges for data owners and security challenges for deep learning model owners. Data owners are concerned about the safety and privacy of their data on MLaaS platforms, while MLaaS platform owners worry that their models could be stolen by adversaries who pose as clients. Consequently, Privacy-Preserving Deep Learning (PPDL) arises as a possible solution to this problem. Recently, several papers about PPDL for MLaaS have been published. However, to the best of our knowledge, no previous paper has summarized the existing literature on PPDL and its specific applicability to the MLaaS environment. In this paper, we present a comprehensive survey of privacy-preserving techniques, starting from classical privacy-preserving techniques to well-known deep learning techniques. Additionally, we present a detailed description of PPDL and address the issue of using PPDL for MLaaS. Furthermore, we undertake detailed comparisons between state-of-the-art PPDL methods. Subsequently, we classify an adversarial model on PPDL by highlighting possible PPDL attacks and their potential solutions. Ultimately, our paper serves as a single point of reference for detailed knowledge on PPDL and its applicability to MLaaS environments for both new and experienced researchers.

Group Key Exchange (GKE) is required for secure group communication with high confidentiality. In particular, a trusted authority can handle issues that happen by the malicious actions of group members, but it is expensive to deploy and not suitable in a dynamic setting where the network requires frequent membership status changes. To overcome these issues, we designed yet another quantumresistant constant-round GKE based on lattice without a trusted authority based on Apon et al.’s protocol (PQCrypto 2019) by modifying their key computation phase. Then, we describe the novel dynamic authenticated GKE (called DRAG) with membership addition/deletion procedures in Ring Learning with Errors (RLWE) setting, while the former ones are built from Diffie-Hellman problem. Under the specific adversary who can leak the long-term secret key from the party, we suggest a rigorous proof of DRAG in the random oracle model based on the hardness assumption of RLWE problem and the property of Rinyi divergence. As a proof of concept, implementation details are described to meet level 1 NIST security. Our implementation is reasonable for practical use since the total runtime to get a group secret key takes about 3 msec and it can be considered as a reference implementation of other quantum-resistant GKEs.