Search Over Encrypted Cloud Data With Secure Updates
2019 IEEE 19th International Conference on Software Quality, Reliability and Security Companion (QRS-C)
Searchable symmetric encryption (SSE) is a key technique which enables searching over outsourced ... more Searchable symmetric encryption (SSE) is a key technique which enables searching over outsourced encrypted data. Hence, SSE must provide for the functions of effective, secure privacy and usability when searching over outsourced data on the cloud. In this regard, various research into the secure search on cloud data has been done using SSE and searchable public key encryption. In this framework, we leverage a practicable searchable encryption (SE) scheme which supports secure and updatable operations on the cloud. The framework will take into consideration the advantages of both SSE and searchable public key encryption in the construction. We also construct an efficient multi-keyword search scheme using k-nearest neighbor (kNN) and Bloom Filter (BF) to achieve ranked search scheme as well as multi-keyword searching.
Uploads
Papers by Selasi Ocansey
curity threats that imperil user privacy. Conventional Deep
Learning methods, relying predominantly on fixed learning rates,
encounter limitations when capturing the nuanced intricacies
of OSN traffic that arise from shifting user behaviors, diverse
content types, and evolving interaction patterns because of
social trending topics changes. To tackle these challenges, our
paper delves into the diverse variations and transitions from
a uniform approach, where a single method is employed for
various types of data, to a multi-variation methodology. This
methodology dynamically adapts to the special characteristics
of each data type, resulting in more effective data represen-
tation while alleviating the limitations associated with fixed-
rate calibration. Therefore, we devise the Adaptive Swarm
Reinforcement Learning (ASRL) method that leverages adaptive
learning to intricately analyze a wide range of user interactions,
endowing our proposed method with the capacity to flexibly
adjust to the constantly shifting OSN patterns. The experiments
show that the proposed ASRL method achieves an accuracy of
98.59% in detecting a range of threat patterns, surpassing other
prevalent methods by an average of 5% across the datasets
from Facebook, Google+, and Twitter. Meanwhile, ASRL logs
suspicious activities to identify the intruder for forensic analysis.
The implementation of our proposed method is now publicly
accessible at https://github.com/don2c/asrl Project.