Adaptive Query Processing Over Encrypted Data Using Blow-fish
Secure Nearest Neighbor Queries, Adaptive IND-CKA security, and Cloud Computing
Processing nearest neighbor queries is a fundamental problem that occurs in many sectors, including machine learning and geographic databases. This research focuses on the Secure Nearest Neighbor (SNN) issue in cloud computing. Prior SNN systems have been ineffective and unsafe. In the present article, we officially establish and empirically demonstrate that the SNN scheme ASPE is truly vulnerable to ciphertext-only attacks. Although previous study showed that building an SNN method is difficult even in significantly permissive standard security models, we highlight the shortcomings of the hardness proof. We present an SNN architecture and show how it can withstand adaptive chosen keyword assaults. Because the complexity of processing queries is exponential, our method is efficient. We created our SNN scheme in C++ and compared its performance with a plain text scheme, a binary scheme, and a PIR scheme on a massive collection of over 10 million real-world data points to determine its efficiency. Experiment findings demonstrate that our scheme is both fast (0.124 millisecond per query when the data set size is 10 million) and scalable in terms of data points.
Article Details
Unique Paper ID: 159035

Publication Volume & Issue: Volume 9, Issue 11

Page(s): 247 - 253
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