Visible to the public Biblio

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Gupta, S., Parne, B. L., Chaudhari, N. S..  2018.  Security Vulnerabilities in Handover Authentication Mechanism of 5G Network. 2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC). :369–374.
The main objective of the Third Generation Partnership Project (3GPP) is to fulfill the increasing security demands of IoT-based applications with the evolution of Fifth Generation (5G) mobile telecommunication technology. In June 2018, the 3GPP has published the study report of the handover architecture and security functions of in 5G communication network. In this paper, we discuss the 5G handover key mechanism with its key hierarchy. In addition, the inter-gNB handover authentication mechanism in 5G communication network is analyzed and identify the security vulnerabilities such as false base-station attack, de-synchronization attack, key compromise, etc. In addition, the handover mechanism suffers from authentication complexity due to high signaling overhead. To overcome these problems, we recommend some countermeasures as pre-authentication of communication entities, delegation of authentication and predistribution of secret keys. This is first work in the 5G handover security analysis. We anticipate that the above security issues and key resilience problem can be avoided from the proposed solutions.
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Buriro, A., Akhtar, Z., Crispo, B., Gupta, S..  2017.  Mobile biometrics: Towards a comprehensive evaluation methodology. 2017 International Carnahan Conference on Security Technology (ICCST). :1–6.

Smartphones have become the pervasive personal computing platform. Recent years thus have witnessed exponential growth in research and development for secure and usable authentication schemes for smartphones. Several explicit (e.g., PIN-based) and/or implicit (e.g., biometrics-based) authentication methods have been designed and published in the literature. In fact, some of them have been embedded in commercial mobile products as well. However, the published studies report only the brighter side of the proposed scheme(s), e.g., higher accuracy attained by the proposed mechanism. While other associated operational issues, such as computational overhead, robustness to different environmental conditions/attacks, usability, are intentionally or unintentionally ignored. More specifically, most publicly available frameworks did not discuss or explore any other evaluation criterion, usability and environment-related measures except the accuracy under zero-effort. Thus, their baseline operations usually give a false sense of progress. This paper, therefore, presents some guidelines to researchers for designing, implementation, and evaluating smartphone user authentication methods for a positive impact on future technological developments.

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Gupta, S., Buduru, A. B., Kumaraguru, P..  2020.  imdpGAN: Generating Private and Specific Data with Generative Adversarial Networks. 2020 Second IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA). :64–72.
Generative Adversarial Network (GAN) and its variants have shown promising results in generating synthetic data. However, the issues with GANs are: (i) the learning happens around the training samples and the model often ends up remembering them, consequently, compromising the privacy of individual samples - this becomes a major concern when GANs are applied to training data including personally identifiable information, (ii) the randomness in generated data - there is no control over the specificity of generated samples. To address these issues, we propose imdpGAN-an information maximizing differentially private Generative Adversarial Network. It is an end-to-end framework that simultaneously achieves privacy protection and learns latent representations. With experiments on MNIST dataset, we show that imdpGAN preserves the privacy of the individual data point, and learns latent codes to control the specificity of the generated samples. We perform binary classification on digit pairs to show the utility versus privacy trade-off. The classification accuracy decreases as we increase privacy levels in the framework. We also experimentally show that the training process of imdpGAN is stable but experience a 10-fold time increase as compared with other GAN frameworks. Finally, we extend imdpGAN framework to CelebA dataset to show how the privacy and learned representations can be used to control the specificity of the output.
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Pandey, A., Mahajan, D., Gupta, S., Rastogi, i.  2020.  Detection of Blind Signature Using Recursive Sum. 2020 6th International Conference on Signal Processing and Communication (ICSC). :262–265.
Digital signatures are suitable technology for public key encryption. Acceptance (non-repudiation) of digital messages and data origin authentication are one of the main usage of digital signature. Digital signature's security mainly depends on the keys (public and private). These keys are used to generate and validate digital signatures. In digital signature signing process is performed using signer's secret key. However, any attacker can present a blinded version of message encrypted with signer's public key and can get the original message. Therefore, this paper proposed a novel method to identify blinded version of digital signature. The proposed method has been tested mathematically and found to be more efficient to detect blind signatures.
Li, M., Wang, F., Gupta, S..  2020.  Data-driven fault model development for superconducting logic. 2020 IEEE International Test Conference (ITC). :1—5.

Superconducting technology is being seriously explored for certain applications. We propose a new clean-slate method to derive fault models from large numbers of simulation results. For this technology, our method identifies completely new fault models – overflow, pulse-escape, and pattern-sensitive – in addition to the well-known stuck-at faults.