Faculty Publications
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Publications by NITK Faculty
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Item Stride-based threat modeling for blockchain-based healthcare supply chain management system(Bentham Science Publishers, 2025) Harish, S.V.; Chandrasekaran, K.; Rathnamma; Divakarla, U.; Ramana, V.The increasing use of blockchain technology in supply chain management has made it imperative to understand the possible security risks associated with its implementation. This research aims to identify important security issues related to supply chain management's use of blockchain technology by doing a thorough analysis of the body of existing literature and looking at actual cases of blockchain deployments. These dangers include the possibility of data privacy breaches, smart contract weaknesses, and 51% attack vulnerability. The report also offers suggestions for reducing these risks, including using multi-factor authentication, regularly carrying out security audits, and enforcing strict access rules. The study's conclusions broaden our knowledge of the security risks associated with blockchain-based supply chain management (BC-SCM) and offer useful guidance to companies thinking about implementing this technology. © 2025 Bentham Science Publishers. All rights reserved.Item Comparative evaluation of algorithms for effective data leakage detection(2013) Kumar, A.; Goyal, A.; Kumar, A.; Chaudhary, N.K.; Kamath S․, S.S.Researchers have proposed several mechanisms to secure data from unauthorized use but there is very less work in the field of detecting and managing an authorized or trustworthy agent that has caused a data leak to some third party advertently or unknowingly. In this paper, we implement methods aimed at improving the odds of detecting such leakages when a distributer's sensitive data has been leaked by trustworthy agents and also to possibly identify the agent(s) that leaked the data. We also implement some data allocation strategies that can improve the probability of identifying leakages and can also be used to assess the likelihood of a leak at a particular agent assuming the fact that the data was not simply guessed by the third party where the leaked data set has been found. We also propose new allocation strategies that work on the basis of No-Wait model, i.e. agent does not need to wait for other agents' allocation and it is different from already proposed model that makes an agent wait for others. These methods do not rely on the alterations of the distributed data, but rather focus on minimizing the overlapping of the allocated data items to various agents, thus facilitating an exact determination of the guilty agent in a particular data leakage scenario. © 2013 IEEE.Item Efficient privacy preserving ranked search over encrypted data(Institute of Electrical and Electronics Engineers Inc., 2016) Praseed, A.; Sudheesh, R.K.; Chandrasekaran, K.Cloud computing and its ever so increasing prominence has rendered it as an unavoidable component for data storage and other data services. The security challenges of storing sensitive data on the cloud is reduced to an extent by the Encryption of data, though in the process of encrypted data search, efficiency is compromised. The encrypted data on the cloud can be retrieved using Searchable Symmetric Encryption (SSE). The current work uses multi-keyword searchable encryption scheme with top-k retrieval to avoid compromises on data privacy occurred by using Order Preserving Encryption schemes. The encryption scheme uses homomorphic encryption and vector space model. The vector space model provides the required search accuracy. The homomorphic encryption allows majority of the computation to be done at the server side while concealing the sensitive data. The user alone can identify the final result of the relevance calculation and request for the actual file. In this paper, phrase searching is included to improve the search results on the encrypted data. To accomplish this we maintain a list of the keyword locations in the encrypted file index. The cloud server, which we assume to be honest-but-curious, operates on these encrypted values and identifies if the words occur in close proximity without knowing the actual locations of these words and the words itself. © 2015 IEEE.Item Evaluation of Machine Learning approaches for resource constrained IIoT devices(Institute of Electrical and Electronics Engineers Inc., 2021) Akubathini, P.; Chouksey, S.; Satheesh, H.S.Resource-constrained devices such as sensors, industrial controllers, analyzers etc., mostly contain limited computational capacity and memory. They are largely deployed in all industries and have been generating a huge amount of data. This data is sent to the cloud servers where various Machine Learning (ML) algorithms are applied to perform the analysis or prediction as per the application. In this process, communication requires bandwidth and time. Since the data is sent into the network, the privacy of the data is not guaranteed. Cloud servers consume a huge amount of power. To reduce these cost factors, the machine learning models are compressed and optimized such that they can fit and run in small footprint devices. The Federated Learning (FL) approach at the edge device level promises to address the data privacy and bandwidth related issues. Since it is a decentralized learning method across a set of devices, the performance of the model also improves. This paper describes and evaluates the machine learning algorithms with various compression methods suitable for resource-constrained IIoT devices and federated learning approach, particularly for time series data applications. Simulation results show that FastGRNN algorithm gives the least model size compared to the traditional RNN algorithms for time series. © 2021 IEEE.Item Abdominal Multi-Organ Segmentation Using Federated Learning(Institute of Electrical and Electronics Engineers Inc., 2024) Yadav, G.; Annappa, B.; Sachin, D.N.Multi-organ segmentation refers to precisely de-lineating and identifying multiple organs or structures within medical images, such as Computed Tomography (CT) scans or Magnetic Resonance Imaging (MRI), to outline boundaries and regions for each organ accurately. Medical imaging is crucial to comprehending and diagnosing a wide range of illnesses for which accurate multi-organ image segmentation is often required for successful analysis. Due to the delicate nature of medical data, traditional methods for multi-organ segmentation include centralizing data, which presents serious privacy problems. This centralized training strategy impedes innovation and collaborative efforts in healthcare by raising worries about patient confidentiality, data security, and reg-ulatory compliance. The development of deep learning-based image segmentation algorithms has been hindered by the lack of fully annotated datasets, and this issue is exacerbated in multi-organ segmentation. Federated Learning (FL) addresses privacy concerns in multi-organ segmentation by enabling model training across decentralized institutions without sharing raw data. Our proposed FL-based model for CT scans ensures data privacy while achieving accurate multi-organ segmentation. By leveraging FL techniques, this paper collaboratively trains segmentation models on local datasets held by distinct medical institutions. The expected outcomes encompass achieving high Dice Similarity Coefficient (DSC) metrics and validating the efficacy of the proposed FL approach in attaining precise and accurate segmentation across diverse medical imaging datasets. © 2024 IEEE.Item Enhancing Healthcare AI with Cross-Silo Personalized Federated Learning on Naturally Split Heterogeneous Data(Institute of Electrical and Electronics Engineers Inc., 2024) Mukeshbhai, A.N.; Annappa, B.; Sachin, D.N.The potential of Artificial Intelligence (AI) in health-care is unavoidable. However, its success depends on the availabil-ity of large, high-quality datasets. Because of data heterogeneity across institutions and privacy concerns, traditional centralized Machine Learning (ML) approaches often face difficulties in this field. Federated Learning (FL) allows collaborative model training without requiring the transfer of sensitive patient data from the original institution. Recent research in FL within the healthcare domain has predominantly relied on centralized datasets, which do not represent real-time data heterogeneity and made assumptions by random data splitting to different medical client institutions. Additionally, it may be challenging for a single global model to encompass the diverse characteristics of various healthcare settings accurately. This paper examines the application of Personalized Federated Learning (PFL) in realistic cross-silo healthcare scenarios with federated natural split datasets in different medical client institutions. This paper discusses the experiments conducted on brain segmentation, survival prediction, melanoma classification, and heart disease di-agnosis. Our experiments show that the proposed PFL techniques consistently improve local model performance over standard FL strategies by up to 10% in different medical use cases. © 2024 IEEE.Item Keyword-based private searching on cloud data along with keyword association and dissociation using cuckoo filter(Springer Verlag service@springer.de, 2019) Vora, A.V.; Hegde, S.Outsourcing of data is a very common scenario in the present-day world and quite often we need to outsource confidential data whose privacy is of utmost concern. Performing encryption before outsourcing the data is a simple solution to preserve privacy. Preferably a public-key encryption technique is used to encrypt the data. A demerit of encrypting data is that while requesting the data from the cloud we need to have some technique which supports search functionality on encrypted data. Without the searchable encryption technique, the cloud is forced to send the whole database, which is highly inefficient and impractical. To address this problem, we consider the email scenario, in which the sender of the email will encrypt email contents using receiver’s public key; hence, only the receiver can decrypt email contents. We propose a scheme that will have encrypted emails stored on the cloud and have capabilities that support searching through the encrypted database. This enables the cloud to reply to a request with a more precise response without compromising any privacy in terms of email contents and also in terms of access patterns. We provide a solution for the email scenario in which we can tag or associate emails with some keywords, and during retrieval, the email owner can request all the emails associated with a particular keyword. Although attempts are seen in the literature to solve this issue they do not have the flexibility of dissociating keywords from an email. Keyword dissociation is essential to modify the association between keywords and emails to enable better filtering of emails. Our technique also supports the functionality of keyword dissociation. The solution allows single-database private information retrieval writing in an oblivious way with sublinear communication cost. We have theoretically proved the correctness and security of our technique. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature.Item Searchable encryption approaches: attacks and challenges(Springer London, 2019) Siva Kumar, D.V.N.; Santhi Thilagam, P.Searchable encryption (SE) is a cryptographic technique that allows the users to perform search over outsourced encrypted data in cloud servers while preserving the privacy of data and search query. In order to preserve privacy and security, data owners use various encryption schemes to encrypt their data and indexes. Due to the vulnerabilities in the encryption schemes adopted by SE schemes, information leakages are possible from the encrypted data. The cloud servers use various information disclosure attacks that exploit these leakages to infer plaintext information. In this paper, we analyze the existing SE approaches with respect to their security and precision goals and possible attacks on them. We also identify the root causes of these attacks and their remedies. An experimental study is also performed to investigate the susceptibility of SE schemes against attacks and countermeasures to prevent them. © 2018, Springer-Verlag London Ltd., part of Springer Nature.Item Observation of resistance switching in Vanadyl-phthalocyanine thin films(Elsevier Ltd, 2020) Raveendra Kiran, M.R.; Ulla, H.; Krishnamanohara; Satyanarayan, M.N.; Umesh, G.Herein, we report the first observation of Negative differential resistance (NDR) associated with resistance switching in Vanadyl-phthalocyanine (VOPc) based devices with the configuration: ITO/F4TCNQ/VOPc/MoO3/Al. It was observed that the devices were initially at low resistance ON state (LS) and were switched to high resistance OFF state (HS) at sufficient applied bias. The NDR behaviour was observed during the initial sweep for each device (often referred to as the writing process). The ON/OFF state transition was attributed to the formation and neutralization of interface dipoles at the ITO/VOPc interface. Finally, the observed non-volatile RS switching behaviour was demonstrated employing impedance spectroscopic studies. This study opens up the potential applications of VOPc Resistance Switching devices in security and data protection applications. © 2020 Elsevier B.V.Item FedCure: A Heterogeneity-Aware Personalized Federated Learning Framework for Intelligent Healthcare Applications in IoMT Environments(Institute of Electrical and Electronics Engineers Inc., 2024) Sachin, D.N.; Annappa, B.; Hegde, S.; Abhijit, C.S.; Ambesange, S.The advent of the Internet of Medical Things (IoMT) devices has led to a healthcare revolution, introducing a new era of smart applications driven by Artificial Intelligence (AI). These advanced technologies have greatly influenced the healthcare industry and have played a crucial role in enhancing the quality of life globally. Federated Learning (FL) has become popular as a technique to create models that can be shared universally using the vast datasets collected from IoMT devices while maintaining data privacy. However, the complex variations in IoMT environments, including diverse devices, data characteristics, and model complexities, create challenges for the straightforward application of traditional FL methods. Consequently, it is not well-suited for deployment in such contexts. This paper introduces FedCure, a personalized FL framework tailored for intelligent IoMT-based healthcare applications operating within a cloud-edge architecture. FedCure is adept at addressing the challenges within IoMT environments by employing personalized FL techniques that can effectively mitigate the impact of heterogeneity. Furthermore, the integration of edge computing technology enhances processing speed and minimizes latency in intelligent IoMT applications. Lastly, this research showcases several case studies encompassing IoMT-based applications, such as Eye Retinopathy Detection, Diabetes Monitoring, Maternal Health, Remote Health Monitoring, and Human Activity Recognition. These case studies provide a means to assess the effectiveness of the proposed FedCure framework and showcase exceptional performance with accuracy and minimal communication overhead, especially in addressing the challenges posed by heterogeneity. © 2013 IEEE.