# Lightweight, Secure, Similar-Document Retrieval over Encrypted Data
###### 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. privacy-preserving similarity detection; document ranking; document fingerprinting; inverted index; Winnowing algorithm; lightweight client +
Footnote †: journal: 17  
articles newly submitted to a journal contain plagiarized work or sections of writing substantially similar to that previously published. However, the current DSD systems presume documents to be public and do not take the privacy of documents to be matched into account. This flaw can impede the effectiveness of the technique in many situations. There are multiple practical circumstances in which it is necessary to detect the similarity measure with a specified questionable document in such a way that the privacy of the content is preserved. For example, to better understand common diseases and epidemics such as COVID-19, as well as the numbers of infected people; different health agencies may wish to jointly verify the similarity of their medical report documents. Privacy dictates that no agency may disclose its report to others. In this instance, it is essential to compare basic reports without breaching privacy guidelines. Furthermore, most journals prohibit double submissions, meaning that the same manuscript may not be submitted to two separate journals at the same time. Thus, the DSD must be able to verify this to be true while still preserving the privacy of each journal. Similarly, conference committees can use privacy-preserving, similarity-detection methods to check the originality of submitted papers. Encryption is the best way to preserve the privacy of sensitive documents when they are uploaded to an untrusted server or shared in a public environment. However, encryption complicates conventional DSD tasks. Hence, it is imperative that an effective, lightweight, and secure DSD technique is designed that can be used with limited-resource devices to measure the similarity between two stored documents in the encrypted domain without violating one's privacy. This paper adopts the concept of syntactic similarity. Fundamentally, when employing this concept, two documents with different keywords will not be considered alike, even if they are synonymous. The alternative approach is a semantic concept which uses complex techniques to examine the meaning of a document during the matching process, which results in high computational cost. This latter concept is outside the scope of this paper. Current DSD methods can essentially be classified into two approaches: hashing [9; 10; 11; 12; 13; 14; 15] and vector space [16; 17].