Scalable Fuzzy Keyword Ranked Search over Encrypted Data on Hybrid Clouds
ABSTARCT :
Searchable encryption (SE) is a powerful technology that enables keyword-based search over encrypted data becomes possible. However, most SE schemes focus on exact keyword search which can not tolerate misspellings and typos. Existing fuzzy keyword search schemes only support fuzzy search within a limited similarity threshold d, the storage cost will grow exponentially or the precision of search results will greatly decrease as $d$ increases. Moreover, the current fuzzy keyword ranked search schemes consider only the keyword weight, and disregard the influence of keyword morphology similarity on the ranking. In this paper, we propose a scalable fuzzy keyword ranked search scheme over encrypted data under hybrid clouds architecture. We use the edit distance to measure the similarity of keywords and design an edit distance algorithm over the encrypted database, in which our scheme achieves fuzzy keyword search for any similarity threshold d with constant storage size and accurate search results. Furthermore, we design a two-factor ranking function combining keyword weight with keyword morphology similarity, which is utilized to rank the search results and enhance system usability. Extensive experiments are performed to demonstrate the trade-off of efficiency and security of the proposed scheme.
EXISTING SYSTEM :
? There is pre-existing security context between each user and the data owner thus authentication between user and data owner is already in place.
? A few existing works exploit tree-based structures to design efficient secure search schemes in different scenarios.
? By incorporating the state-of-the-art information retrieval technique, the proposed MTS schemes enjoy the same flexibility and search result accuracy as the existing state-of-the-art multi-keyword ranked search over plaintext.
? Privacy breach is still likely to occur owing to the existence of disgruntled, profiteered or curious employees from CSP.
DISADVANTAGE :
? Based on their two clouds architecture, we consider to address the privacy-preserving fuzzy keyword search problem simultaneously supporting verifiability of search result in hybrid cloud model.
? We also present one integrated solution, which hopefully offer more insights into this important problem.
? The management of the decryption keys of the returned files is an orthogonal problem and has been studied separately.
? The main problem of this scheme is that the final search results inevitably contain false positive due to bloom filter being the underlying index construction technique.
? Research along this line includes and it aims to provide a more general solution to the secure computation on the untrusted cloud server problem.
PROPOSED SYSTEM :
• They extend the proposed secure keyword search to multi-user setting, where an encrypted index can be searched by various users holding different private keys.
• To improve the search efficiency, many secure search schemes have been proposed, where queries can be executed over encrypted indexes (rather than encrypted data themselves) by users who possess proper “trapdoors”.
• The proposed schemes can meet various stringent privacy requirements while retaining effective search functionalities.
• Due to the proposed search algorithm and tree-based index structure, the baseline search is far efficient than.
ADVANTAGE :
? The private cloud performs the security-critical operations, whereas the public cloud performs the performance-critical ones.
? Through rigorous security and efficiency analysis, we show that our proposed scheme is secure under the proposed model, while correctly and efficiently realizing the verifiable fuzzy keyword search.
? Most are focused on efficiency improvements and security definition formalizations.
? Although the time cost is not very low,the index construction process can be conducted off-line, it will not affect the searching efficiency.
? However, this raises a new challenge for performing search over the encrypted data efficiently.
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