Lightweight, Secure, Similar-Document Retrieval over Encrypted Data
Source abstract
Abstract
Applications for document similarity detection are widespread in diverse communities, including institutions and corporations. However, currently available detection systems fail to take into account the private nature of material or documents that have been outsourced to remote servers. None of the existing solutions can be described as lightweight techniques that are compatible with lightweight client implementation, and this deficiency can limit the effectiveness of these systems. For instance, the discovery of similarity between two conferences or journals must maintain the privacy of the submitted papers in a lightweight manner to ensure that the security and application requirements for limited-resource devices are fulfilled. This paper considers the problem of lightweight similarity detection between document sets while preserving the privacy of the material. The proposed solution permits documents to be compared without disclosing the content to untrusted servers. The fingerprint set for each document is determined in an efficient manner, also developing an inverted index that uses the whole set of fingerprints. Before being uploaded to the untrusted server, this index is secured by the Paillier cryptosystem. This study develops a secure, yet efficient method for scalable encrypted document comparison. To evaluate the computational performance of this method, this paper carries out several comparative assessments against other major approaches.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Spam and Phishing Detection · Advanced Steganography and Watermarking Techniques · Cryptography and Data Security
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
College of Big Data and Internet, Shenzhen Technology University, Shenzhen 518118, China
Computer Technology Engineering Department, Iraq University College, Basrah 61004, Iraq
Department of Computer Science, College of Education for Pure Sciences, University of Basrah, Basrah 61004, Iraq
Shenzhen Institute, Huazhong University of Science and Technology, Shenzhen 430074, China
Faculty of Biological and Physical Sciences, Tom Mboya University College, Homabay 40300, Kenya
School of Cyber Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Iraqi Cement State Company, Ministry of Industry and Minerals, Baghdad 10011, Iraq
Funding
This work is supported by Natural Science Foundation of Top Talent of SZTU (grant no. 656 20211061010016)
References (43)
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