A privacy protection-oriented parallel fully homomorphic encryption algorithm in cyber physical systems

Abstract

Cyber physical system (CPS) is facing enormous security challenges because of open and interconnected network and the interaction between cyber components and physical components, the development of cyber physical systems is constrained by security and privacy threats. A feasible solution is to combine the fully homomorphic encryption (FHE) technique to realize the efficient operation of ciphertext without decryption. However, most current homomorphic encryption algorithms only support limited data types, making it difficult to be widely applied in actual environment. To address this limitation, we propose a parallel fully homomorphic encryption algorithm that supports floating-point numbers. The proposed algorithm not only expands the data types supported by the existing fully homomorphic encryption algorithms, but also utilizes the characteristics of multi-nodes in cloud environment to conduct parallel encryption through simultaneous group-wise ciphertext computations. The experimental results show that, in a 16-core 4-node cluster with MapReduce environment, the proposed encryption algorithm achieves the maximum speed-up exceeding 5, which not only solves the limited application problem of the existing fully homomorphic encryption algorithm, but also meets the requirements for the efficient homomorphic encryption of floating-point numbers in cloud computing environment.

Privacy-Preserving Schemes for Safeguarding Heterogeneous Data Sources in Cyber-Physical Systems

“…Its overall performance reveals that its computational overhead does not increase as much as those in the original methods but its data utility cannot be retained as there are no further analysis is conducted on the ciphertext. In [98], a method based on a parallel FHE algorithm which works on floating-point numbers, not only integers, and can eliminate security threats using any out-oforder ciphertexts is proposed. As homomorphic algorithms have low efficiency, this scheme uses a MapReduce platform through data blocks.…”

Cloud Computing Trends: A Literature Review

“…Working solutions to address data privacy in cloud computing apply encryption and decryption algorithms such as a homomorphic encryption algorithm, which has the serious limitation of supporting only limited data types. To handle this problem, Min, Yang, Sangaiah, Bai, and Liu (2019) proposed a parallel fully homomorphic encryption algorithm that supports floating-point numbers. The proposed algorithm uses the characteristics of multi-nodes in the cloud environment to conduct parallel encryption through simultaneous group-wise ciphertext computation.…”

A Comprehensive Survey on Secure Outsourced Computation and Its Applications

“…The precision of the calculations is almost the same as the unencrypted case. Scheme [155] realized parallel FHE algorithm for floating-point number operations, based on the MapReduce environment. Moreover, to provide stronger security, the order of ciphertexts is disrupted to eliminate the relevance between the child ciphertext and key pair.…”