Srinivasan Krishnaswamy

Jaydip Sen, Joceli Mayer, Subhasis Dasgupta et al.

In the era of generative artificial intelligence and the Internet of Things, while there is explosive growth in the volume of data and the associated need for processing, analysis, and storage, several new challenges are faced in identifying spurious and fake information and protecting the privacy of sensitive data. This has led to an increasing demand for more robust and resilient schemes for authentication, integrity protection, encryption, non-repudiation, and privacy-preservation of data. The chapters in this book present some of the state-of-the-art research works in the field of cryptography and security in computing and communications.

Subrata Nandi, Srinivasan Krishnaswamy, Behrouz Zolfaghari et al.

In this paper, we propose and evaluate a method for generating key-dependent feedback configurations (KDFC) for $σ$-LFSRs. $σ$-LFSRs with such configurations can be applied to any stream cipher that uses a word-based LFSR. Here, a configuration generation algorithm uses the secret key(K) and the initialization vector (IV) to generate a feedback configuration. We have mathematically analysed the feedback configurations generated by this method. As a test case, we have applied this method on SNOW 2.0 and have studied its impact on resistance to various attacks. Further, we have also tested the generated keystream for randomness and have briefly described its implementation and the challenges involved in the same.

Uddipana Dowerah, Srinivasan Krishnaswamy

We propose a multi-bit leveled fully homomorphic encryption scheme using multivariate polynomial evaluations. The security of the scheme depends on the hardness of the Learning with Errors (LWE) problem. For homomorphic multiplication, the scheme uses a polynomial based technique that does not require relinearization (and key switching). The noise associated with the ciphertext increases only linearly with every multiplication.

Uddipana Dowerah, Srinivasan Krishnaswamy

We propose a symmetric key homomorphic encryption scheme based on the evaluation of multivariate polynomials over a finite field. The proposed scheme is somewhat homomorphic with respect to addition and multiplication. Further, we define a generalization of the Learning with Errors problem called the Hidden Subspace Membership problem and show that the semantic security of the proposed scheme can be reduced to the hardness of this problem.

Srinivasan Krishnaswamy, Harish K. Pillai

In this paper we deal with the dimension of multisequences and related properties. For a given multisequence W and an m tuple of positive integers R, we define the R extension of W. Further we count the number of multisequences W whose R extensions have maximum dimension and give an algorithm to derive such multisequences. We then go on to use this theory to count the number of Linear Feedback Shift Register(LFSR) configurations with multi input multi output delay blocks for any given primitive characteristic polynomial and also to design such LFSRs. Further, we use the result on multisequences to count the number of Hankel matrices of any given dimension.