# Lightweight, Secure, Similar-Document Retrieval over Encrypted Data
## 2 Related Works  
The creation of the first method for detecting similarity in documents is credited to [PERSON] [9]. His method uses the concept of hashing to assess the similarities within an extensive collection of documents, whereby a series of fingerprints is obtained from the substrings of a document and then compared with others to detect similarities. Subsequently, [PERSON] et al. [10] applied the concept of the fingerprint to sentences. This procedure has been proven to successfully detect restructuring within phraseology. [PERSON] [11] proposed a method of plagiarism detection based on the quantity of words present in both documents within relative sentences in order to assess the level of similarity between the two texts. [PERSON] et al. [12] developed a systematic algorithm for confined fingerprinting, known as the Winnowing algorithm, which extracts a fundamental property from a fingerprint set which is guaranteed to detect documents with similarities and decreases the time complexity of the search query. [PERSON] et al. [13] employed this algorithm in order to develop a methodology for detecting intellectual theft among collections of textual work. [PERSON] and [PERSON] [25] latterly propounded a form of concise document rendition, which could be employed to effectively discriminate duplicate and almost duplicate documents from result lists. Another system for uncovering intellectual theft has been suggested by [PERSON] and [PERSON] [26]. Their concept consists of five principle elements, which are: pre-processing,candidate retrieval, phraseology gradation, semantic role labelling, and comparability detection. The first element, pre-processing, is composed primarily of sentence partition, which separates the language into multiple phrases and then eliminates stop words, which are the most commonly used words in the language. The next stage, candidate retrieval, involves the collection of a selection of texts comparable to the one being queried, utilizing n-grams and the Jaccard coefficient to gather the documents. After this, the sentence-ranking step generates a hierarchy of phraseology in the document being investigated in order to delineate original and questionable sentence pairings, while the resultant similarity level is determined via the principle of cosine similarity. Subsequently, the Semantic Role Labelling phase seeks to identify, based on the semantic association of its parts, the function in terms of the meaning of each section of a sentence, such as, for instance, the phrase's subject and object. This mode of discernment targets shared meanings. The final stage, similarity detection, examines the ordered suspected and original phrases for comparability. Sections of other texts are placed against each section of the examinee text to produce results. Assessment has demonstrated that the pre-generated hierarchy of phrases using the sentence ranking step may enhance the efficacy of the system, yet it is costly in terms of the computation needed. There is little extant research on the subject of secure DSD over encrypted data. The EsPRESSo protocol, proposed by [PERSON] et al. [14], implemented private set intersection cardinality (PSI-CA) and Jaccard similarity based on N-grams to ensure a privacy-preserving DSD. While EsPRESSo adopts MinHash to increase time efficiency, it unfortunately reduces document retrieval accuracy [27]. The work of [PERSON] et al. is far from being scalable, especially if large data or big data are adopted [28], while the authors do not take into account the scope of the compromised threat model. Instead, [PERSON] et al. took a pragmatic approach that emphasises ensuring that an adversary claiming to be either the server or the client gets the minimum amount of information by engaging in the scheme [29]. Thus, there can be no guarantee that parties with malicious behaviour intent can retain the same input through multiple interactions, so it remains an open and interesting problem.