# Lightweight, Secure, Similar-Document Retrieval over Encrypted Data
###### Abstract
In the hash policy, a set of fixed-length substrings is extracted from the document. Then, the hash code is computed for each substring. Finally, a descriptive compressed fingerprint is generated from the hash codes. Two documents are considered similar if they contain a large number of shared fingerprint terms over and above a predefined threshold. This approach is best suited to capturing local similarities, that is, finding any overlapping content between two documents. The alternative, vector space, approach is to employ information retrieval (IR) concepts to find and measure global similarity information. In the vector space model, each document is represented as a vector for terms or words, and each vector entry indicates the specific frequency information for the corresponding term. In this model, two documents are considered similar if they contain common terms which are repeated. However, the vector space model detects the similarity of entire documents, and can therefore identify two documents as being similar if they contain the same bag of words, even though the content of the documents is dissimilar. Compared with global similarity, local similarity characteristically gains more accurate results for documents to be retrieved. That is why the local similarity is adopted in our work [18; 19]. The proposed scheme uses the hashing approach to generate a fingerprint for each document, where the fingerprint is a representative, yet compressed set of numbers. Once the fingerprint set of the entire document is defined, an inverted index structure can be built. The inverted index structure is used extensively and is widely employed in the information retrieval community [20; 21] to provide fast data retrieval in a scalable manner for large document collections, as well as supporting fast query processing [22; 23; 24]. This study adopted a Winnowing algorithm to extract and select only fundamental properties from a fingerprint set, which not only significantly facilitates the secure search query, but also reduces space complexity, which results in low time-consumption. The inverted index structure consists of a set of fingerprint terms and their corresponding posting lists. Each list includes a set of document IDs that contain the corresponding fingerprint term. In order to implement the benefits of the inverted index whilst also safeguarding data, a secure, inverted index is created, and then a secure DSD methodology can be employed over that index. At this stage, a secret key is used to encrypt the index of fingerprints in such a manner that similarity detection can take place without revealing the contents of the underlying data. Only those in possession of the key can generate a valid fingerprint for those documented, which are to be matched. This paper's contribution can be summarised as follows. First, it will demonstrate how documents can be compared in encrypted domains in a lightweight manner without compromising privacy and revealing the plain data. Second, it develops an efficient and secure solution to compute the common fingerprint terms between the document provided and the entire stored collection; subsequently, the paper will describe how the fingerprint approach can be employed innovatively to generate a secure inverted index, upon which a secure and lightweight privacy-preserving DSD can be constructed. This significantly simplified process results in a low client-side response time. Third, this method is appropriate for lightweight client implementation with limited-resource devices, with low complexity for query generation and a smaller key size for the encryption and decryption process of similar retrieved documents. It also allows for the smallest and most appropriate hash value to be selected by adopting a Winnowing algorithm, incurring low space complexity and low client response times. Fourth, the method proposed in this paper maintains the complexity of linear computation while increasing the scale of document collections.