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community. Interest communities will reorganize as any user's interest has changed. Simulation results show this model has higher search efficiency and lower search cost compared with traditional keyword search algorithm.
privacy before outsourcing. The state-of-the-art SSE schemes search only over encrypted data through keywords, hence they do not provide effective data utilisation for large dataset files in cloud. We propose a Most Significant Index Generation Technique(MSIGT), that supports secure and efficient index generation time using
Searchable symmetric encryption (SSE) enables us to search encrypted data with an arbitrarily chosen keyword without leaking information on the data and keyword. SSE is expected to be used in, for example, cloud computing and genome analyses. In particular, privacy of genome data must be guaranteed for long periods
service failure, inefficient application, and privacy problem when they are applied to the multi-cloud applications. In this paper, we propose a Privacy-preserving Similarity Search scheme termed PSS. We exploit the n-grams method and counting bloom filters to define and compute the keyword-order. Based on this order, all
compromised. The encrypted data on the cloud can be retrieved using Searchable Symmetric Encryption (SSE). The current work uses multi-keyword searchable encryption scheme with top-k retrieval to avoid compromises on data privacy occurred by using Order Preserving Encryption schemes. The encryption scheme uses homomorphic
searchable encryption scheme. And the model introduces a trusted third authority (TA), which is independent from the honest but curious cloud server. It allows multi-keyword search and informs data integrity verify results on the cipher text and outsourced data to users simultaneously. Finally, we demonstrate that the new
Polynomial evaluation is an important tool in constructing many cryptographic protocols, such as proof of retrievability and verifiable keyword search. However, for the high degree polynomials derived from very large datasets, polynomial evaluation becomes an intractable problem, especially for resource limited
In the past few years, videos become an ordinary communication mean for both personal and business activities. Not only the keyword search that have been utilized widely, but also the video content-based search, i.e. given a query video, the similar video sequences can be retrieved. Meanwhile, the increasing of the
This paper proposes a novel personalized news recommendation system named InfoSlim. The new system uses semantic technique to annotate news items and user preference in order to add rich metadata information into traditional keyword vector. By doing this, the similarity measure between item profile and user profile
gambling is under strict regulations. However, there are so many websites that it is rather difficult to regulate Internet gambling and rather challenging to identify them. It may introduce many false positives or false negatives, if we simply grep contents of websites with keywords. In this paper, we find that the behavior
for each CP/ST process so that not only the execut n time of each process but also the power consumption of servers can be reduced. We show the evaluation of the EA algorithm in terms of the total power consumption and average execution time. Keywords-Power consumption models, Energy-aware (EA) selection algorithm
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