Genomic Data Fusion using Paillier Cryptosystem

Shobha P.; Nalini N.

doi:10.59796/jcst.V14N3.2024.57

Authors

Shobha P. Computer Science and Engineering Department, Nitte Meenakshi Institute of Technology (Affiliated to Visvesvaraya Technological University), Bengaluru, Karnataka, 560064, India
Nalini N. Computer Science and Engineering Department, Nitte Meenakshi Institute of Technology (Affiliated to Visvesvaraya Technological University), Bengaluru, Karnataka, 560064, India

DOI:

https://doi.org/10.59796/jcst.V14N3.2024.57

Keywords:

Artificial Intelligence, data Fusion, differential privacy, data privacy, homomorphic, medical, paillier cryptosystem, radar data set

Abstract

The proposed work performs secure data fusion using homomorphic encryption, specifically the Paillier cryptosystem. The Paillier cryptosystem allows computation to be performed on encrypted data without decrypting it first, thus ensuring the privacy and security of the computation. The experiment measures the algorithm's performance based on execution time, memory usage, security, accuracy, and scalability. The data-level Paillier cryptosystem approach is generally slower than the feature-level fusion method due to its more complex operations and computations. Scalability is limited by the time required for encryption, homomorphic addition, and decryption. Improving scalability can be achieved by parallelizing the encryption and decryption steps, optimizing the homomorphic addition algorithm, or using more efficient cryptographic primitives. The article compares the performance of the Paillier cryptosystem with differential privacy in terms of their advantages and disadvantages. By adopting a preemptive approach to data fusion security, healthcare organizations can minimize the risk of data breaches and protect patient privacy. Data fusion security is an important factor when dealing with medical records. In the field of medical records, data fusion refers to the method of combining multiple sources of data into a distinct record. This can include data from electronic health records (EHRs), medical imaging devices, wearable devices, and other sources. There are several security considerations that must be addressed when fusing data from multiple sources.

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