Faculty Publications
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Publications by NITK Faculty
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Item Design and implementation of secure Internet based voting system with user anonymity using Identity Based Encryption System(2009) Purushothama, B.R.; Pais, A.R.With Internet becoming ubiquitous, electronic transactions over the Internet have become an integral part of day to day life. The Internet is used for more and more secure transactions like banking, shopping, submitting tax returns etc. In a way, the need for a secure Internet based electronic voting system is an obvious demand. The task of designing a secure Internet based voting system is a cryptographic challenge. This paper proposes and discusses the design and implementation of secure Internet based electronic voting system using Identity Based Encryption System (IBES). This proposed system satisfies various security requirements like, privacy, anonymity, eligibility, accuracy, fairness, uniqueness, verifiability and receipt freeness. Total user anonymity is achieved using IBES. © 2009 IEEE.Item ESIS-SSI: Efficient (n,n) Secret Image Sharing with Shrinking Shadow Images(Institute of Electrical and Electronics Engineers Inc., 2024) Purushothama, B.R.The problem of secret image sharing has been extensively studied, and the size of the shadow images is a critical factor for applying these schemes in resource-limited environments. Most existing schemes generate shadow images that are approximately the same size as the original secret image. This work addresses the issue of reducing shadow image size. A (n, n) threshold scheme that produces smaller shadow images is proposed. The new method for sharing the secret image results in shadow images of size ⌈nh ⌉ × w, as opposed to the standard h×w size in most existing schemes. The secret key is not needed at the Combiner while reconstructing I. The proposed scheme has been rigorously validated and implemented, demonstrating both efficiency and security, with shadow images not disclosing any information about the original secret image. Compared to existing methods, our scheme is shown to generate the shadow images with the reduced size and is efficient. © 2024 IEEE.Item Multilevel Security Framework with Fault Tolerance using Secret Image Sharing and Steganography(Institute of Electrical and Electronics Engineers Inc., 2024) Gound, Y.S.; Purushothama, B.R.Secure data embedding within digital images has become crucial for protecting sensitive information against unauthorized access. In this work, we propose a multilevel security that integrates data embedding, two-level secret sharing, and message extraction within digital images. The embedding process involves encoding data into grayscale images using random least significant bit substitution, ensuring covert integration while preserving image quality. Subsequently, a multilevel secret sharing scheme based on Shamir’s secret sharing is applied to generate multiple shares of the image, enhancing security through distributed storage and threshold-based reconstruction. The extraction phase employs polynomial interpolation to reconstruct the original image from the shares, facilitating seamless retrieval of embedded data. The adversary will not be able to obtain any information about the secret without significant computation cost. © 2024 IEEE.Item A Practical and Efficient Key-Aggregate Cryptosystem for Dynamic Access Control in Cloud Storage(Springer Science and Business Media Deutschland GmbH, 2024) Pareek, G.; Purushothama, B.R.Dynamically changing access rights of users in large-scale secure data sharing is an important challenge which designers of the secure systems have to address. We focus efficient enforcement of the dynamic access control using key-aggregate cryptosystem (KAC), an efficient solution to secure data sharing. In this paper, we present a novel KAC construction that, in addition to satisfying all key-aggregate efficiency requirements, allows a data owner to enforce dynamic updates in access rights of a user much more efficiently than the existing ones. In particular, the proposed KAC construction handles the dynamic updates at the level of public parameters, and does not require the data owner to carry out any secure transmissions. This further means that none of the data users, including the one(s) whose access rights are updated, has to update their secrets. Thus, the dynamic update operation of the proposed KAC scheme is free from the one-affects-all problem. We present a formal security proof of the proposed KAC scheme and analyze its performance to further support our claims. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.Item Enhancing Data Security and Privacy through Blockchain and Machine Learning(Institute of Electrical and Electronics Engineers Inc., 2024) Machhale, G.; Purushothama, B.R.; Modi, C.N.Blockchain and Machine Learning (BML) are two of the most rapidly advancing technologies that are revolutionizing various industries worldwide. Blockchain is a widely known decentralized, immutable technology that provides a transparent, safe method of exchanging and storing data. In contrast, Machine Learning presents organizations with predictive analysis and automated decision-making abilities, enabling them to derive valuable insights from data. This study looks at how blockchain technology and machine learning are combining to try to improve data security and privacy. We have proposed a novel framework with the required components which integrates blockchain and machine learning. We have compared our proposed framework with ACO and SVM implemented frameworks with accuracy metric. Our system gave higher accuracy than former. Later we have provided discussion which encompasses the advantages, obstacles, and future implications of this integration, with a particular emphasis on its applications within the healthcare and financial industries. © 2024 IEEE.Item AI-Powered Cryptanalysis: Identifying Encryption Algorithms and Recovering Plaintext(Institute of Electrical and Electronics Engineers Inc., 2025) Simhadri, S.; Raghavendra; Purushothama, B.R.With encryption becoming more prevalent for the security of digital correspondence, the actual process of analyzing the ciphertext without the decryption key becomes one of the single biggest problems in cybersecurity and cryptanalysis. This represents two fundamental problems: classifying ciphertext based on the encryption scheme used, and reconstructing plaintext from encrypted sequences leveraging deep learning. The more classic style approaches to cryptanalysis often rely on brute force or some mathematical 'weakness' in the algorithm itself, but with the advent of neural networks, the cryptanalysts are able to discover patterns to the structural data represented in the encrypted data. This paper deploys the bidirectional long short-term memory (BiLSTM) and bidirectional gated recurrent unit (BiGRU) neural networks to classify ciphertext produced by the Advanced Encryption Standard (AES), Triple Data Encryption Standard (3DES), Blowfish, and Twofish encryption schemes into the respective categories. The BiLSTM model was able to classify the ciphertext with a 87.91% classification accuracy for the dataset, with the 1.07 % better performance over the BiGRU model, which successfully classified the dataset with 86.98% accuracy. The second part of the research involved the use of a sequence-to-sequence long short-term model to reconstruct original text from ciphertexts encrypted under the Data Encryption Standard (DES) and Twofish - plaintext was provided from the Internet Movie Database (IMDB) dataset. The reconstruction accuracy of DES-encrypted ciphertext was high, achieving an F1-score of 0.868, which supports that certain encryption schemes may retain exploitable patterns on which deep learning models can be trained. In contrasting examples, the Twofish-decrypted ciphertext was lowered to an F1-score of 0.750 resulting in a lower F1 by 13.6% due to heavier diffusion which produced additional resistance. The above findings demonstrate the efficacy of neural models to detect and exploit structural weaknesses in legacy encryption systems and call for encryption algorithms to reduce recoverable features against deep learning attacks. The study provides the first step for future studies involving artificial intelligence driven tools assisting in forensic cryptography, automated vulnerability assessment, and secure system design. © 2025 IEEE.Item Proving the (In)Security of CRT Based Key Management Schemes Under SAOA Model(Springer, 2024) Sharma, P.; Purushothama, B.R.There have been several proposed methods in the literature for securely distributing group keys and managing group dynamics for secure group communications. While these methods claim to be secure against passive adversaries, our focus has been on a more powerful adversary known as a strong active outsider adversary. This adversary has the ability to corrupt legitimate users, which can result in the leakage of crucial secret information to the adversary. Such information can enable the adversary to recover both current and past group keys. One commonly utilized approach for ensuring secure group communication is group key management schemes based on the Chinese remainder theorem (CRT). In this paper, we evaluate prominent CRT-based key management schemes in the presence of an active adversary. Our findings indicate that the adversary can exploit the leaked information of the corrupted user to break backward secrecy. As a result, we demonstrate that the CRT-based schemes found in the literature are insecure against strong active adversaries and are therefore unsuitable for practical applications. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.