SEARCHING ENCRYPTED DATA USING KNN ALGORITHM

SEARCHING ENCRYPTED DATA USING KNN ALGORITHM
Author(s):
REMYA R S
Keywords:
cloud, SEDESE, Encryption, ABE, Dynamic searching algorithm, K-Nearest neighbor, medical, secure
Abstract
In medical cloud computing, a patient can remotely export her medical records to a cloud server. In this case, only authorized physicians are allowed access due to the extremely sensitive nature of the medical data. One common practice is to encrypt the data prior to outsourcing; in this scenario, the patient just needs to give the encryption key to the approved medical professionals. Nevertheless, this significantly limits the value of outsourced medical data because it is challenging to search through the encrypted data. This research proposes two Secure and Efficient Dynamic Searchable Symmetric Encryption (SEDESE) algorithms for medical cloud data. First, we suggest a secure k-Nearest Neighbor (kNN) and attribute-based encryption (ABE) approach that is dynamically searchable and symmetric. Two very difficult-to-obtain security properties in the dynamic encryption domain are forward privacy and backward privacy, which this approach can provide.
Article Details
Unique Paper ID: 163045 Publication Volume & Issue: Volume 0, Issue no Page(s): 274 - 279
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