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@article{176607,
        author = {P Priya and Dr.D.Maya and Mr.G.Rajamuneeswaran and Ms.S.Madhumathi},
        title = {ENHANCED ATTRIBUTE-BASED ENCRYPTION WITH FORWARD SECURITY FOR CLOUD ASSISTED IOT},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {5982-5988},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=176607},
        abstract = {When cloud computing first emerged, data owners were encouraged to move their intricate data management systems from local locations to commercial public clouds to save money and gain greater flexibility. However, to preserve data privacy, sensitive information must be encrypted prior to outsourcing, rendering conventional data utilization based on an outdated plaintext keyword search. Thus is crucial to enable an encrypted cloud data search service. To meet the need for effective data retrieval, the search engine must support multi-keyword queries and offer result similarity ratings, particularly given the vast number of data users and documents in the cloud. Search results are rarely differentiated in related works on searchable encryption, which concentrate on single-term or Boolean keyword searches. In this study, we define and solve the difficult problem of privacy-preserving multi-keyword ranked ontology keyword mapping and search over encrypted cloud data (EARM) for the first time. A loosely connected and expressive communication method for large-scale distributed systems is content-based publishing/subscribing.
Through the use of Bloom filters and basic randomization techniques, this process lowers the cost of encrypted matching. Provide a thorough security analysis of the data that Bloom filters in this instance released, along with confinement obfuscation approaches. Among the many multi-keyword semantics, we select the effective "Enhanced Association Rule Mining coordinate matching" principle, which states that the goal is to capture as many matches as possible between the search query and the data documents. We then use "inner product similarity" to quantitatively formalize this principle for measuring similarity. After introducing a simple EARM system that uses safe inner product computing, we greatly enhance it to satisfy various privacy needs in two threat model tiers.},
        keywords = {Attribute-Based Encryption, Forward Security, Puncturable Encryption, Searchable Encryption.},
        month = {May},
        }