Lattice-based Functional Encryption for Next Generation Privacy-Preserving Computation

With the rise of cloud-based data processing and multi party computations, the demand for secure, sturdy, and effective privacy-preserving tasks has risen. Homomorphic Encryption (HE) and Bilinear Functional Encryption (FE) users face computational and properties constraints in the form of high
computation overheads, high ciphertext expansions, and lower functionality accuracy reducing usability. This research wishes to avoid such limitations by developing Lattice-Based Functional Encryption (FE) framework, which takes into consideration the neighborhood of security, efficiency and accuracy. Constructing a series of structured lattice based schemes for key generation and ciphertext formatting, allows for encrypted data evaluation without any need. Experimental evaluations based on
structured datasets, time of encryption, time of queries evaluation, ciphertext length, and accuracy of functions were observed. Lattice-Based FE achieved a total time of 7.5 ms/1 KB encryption time, 14.3 ms/ query evaluation time, 2.1 KB/ 1 KB ciphertext size and resulted in 99.0% functional accuracy as
compared to HE and Bilinear FE methods. The findings show that the lattice-based FE was able to deliver scalable and effective privacy-preserving computations. This framework moreover will provide a promising approach for secure data processing tasks based on the IoT, finance and cloud interest, ushering an expansion into more cryptographic methods for years to come. Keywords: Lattice-Based Functional Encryption, Homomorphic Encryption, Privacy-Preserving Computation, Ciphertext
Efficiency, Encrypted Data Evaluation, Secure Computation