INSTITUTIONAL REPOSITORY
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Tkbe : Two Key Broadcast Encryption For The Iot
(2023-07) Parikh, Rachit
The growing usage of the Internet of Things (IoT) has made it necessary to ensure the security of these interconnected devices. Key management becomes particularly challenging when devices are not always online due to resource constraints or business decisions. Moreover, the IoT infrastructure typically relies on the publish-subscribe model for communication, which raises additional security considerations since the message broker becomes a central point of attack. Existing solutions with end-toend encryption from publisher to subscriber are either computationally expensive for resource constrained devices or compromise on the decoupling in publish/subscribe systems. This thesis tackles the problem of efficient key management in IoT systems by employing techniques from broadcast encryption and proposes a lightweight framework - TKBE (Two Key Broadcast Encryption) that reduces trust in the broker and enhances security in IoT communications while providing efficient immediate revocation with decoupling and offline key updates.
Sentiment Analysis on Hindi-English Code-mix Data
(Indian Statistical Institute, 2023-06-23) Singh Bisht, Balwant
Social media has emerged as a prominent platform for expressing opinions, leading to the development of a unique language known as code-mix text. This form of language incorporates words from multiple languages, such as Hindi and English in India. While sentiment analysis techniques have achieved moderate success in handling English texts, the same level of effectiveness has not been attained when dealing with code-mix text. In this study, we propose deep learning techniques to address the challenges of sentiment analysis in code-mix Hindi-English text data. Leveraging a pre-trained cross-lingual large language model called XLM-RoBERTa, we employ a transfer learning approach. Four distinct approaches are employed to train the model for sentiment analysis on a Hinglish dataset. The first approach involves training the model using the Hinglish dataset exclusively. The other three approaches utilise mixed datasets, where one includes the augmentation of Spanish-English and Marathi-English datasets with the Hinglish dataset, the second approach solely relies on the mixed dataset without Hinglish data, while the final approach exclude the Spanish-English data. The trained models are evaluated on the same Hinglish dataset, and their performance is compared. The results indicate that the approach of increasing the training data by arbitrarily combining different kinds of mixed datasets does not yield improvements over previous findings. But combining the data of languages with similar linguistic characteristics can result in better performance. This highlights that the problem associated with scarcity of data for code-mixed languages can be effectively solved by using data of similar languages. In conclusion, our study emphasises the ongoing challenge of limited data for code-mixed languages. We demonstrate that augmenting the training data with various mixed datasets does not lead to enhanced performance but the data of similar languages can be combined to produce better outcomes. These findings provide valuable insights for future research in sentiment analysis of code-mix text.
On the Jordan-Chevalley-Dunford Decomposition of Certain Classes of Operators and Convergence of Their Normalized Power Sequences
(Indian Statistical Institute, 2026-02-25) Shekhawat, Renu
The classical Jordan–Chevalley decomposition expresses a matrix A ∈ Mn(C) as a unique commuting sum A = D + N, where D is diagonalizable and N is nilpotent. Although this decomposition is algebraic in origin, it encodes significant spectral information and, as shown by Nayak, has an important analytic consequence: the convergence of the normalized power sequence {|A^n|^ 1/n }n∈N ; |A| := (A∗A)^1/2 . In this thesis we study Jordan-Chevalley–type decompositions in infinite-dimensional settings and their connection with the convergence behaviour of normalized power sequences. In particular, we discuss this phenomenon for Dunford’s spectral operators and compact operators on a complex Hilbert space, and further extend the theory to operators affiliated with finite type I von Neumann algebras.
Innovations in Graph Neural Network Design: Addressing Oversmoothing, Heterophily, and Information Propagation
(Indian Statistical Institute, 2026-04-13) Bose, Kushal
In an unstructured learning paradigm, Graph Neural Networks (GNNs) adeptly tackle graph data like social networks, molecules, transaction networks, etc. In the primitive stage, GNNs are designed to be shallow, comprising two or three layers. Emulating the success of deep CNNs, deep GNNs are also proposed by stacking multiple layers. Those multi-layered GNNs are pivotal in enabling long-range interactions where multi-hop neighbors carry significant information, like molecular property prediction. Yet, the multi-layered GNNs face challenges of Oversmoothing, where node features become indistinguishable due to the recursive nature of message passing. In the second chapter of the thesis, we propose a non-recursive message passing technique to address oversmoothing. Our method explores random paths and computes path features, and those are subsequently aggregated to update the node features. The multi-hop message passing also depends on the homophily or heterophily settings of the network. GNNs typically perform better in homophilic settings where adjacent nodes share identical class labels. Conversely, the performance of GNNs is exacerbated in the heterophilic networks where adjacent nodes may have different class labels. In the third chapter of the thesis, we address the challenges of graph heterophily by rewiring the graph topology. We learn the similarity scores of the edges obtained from the autoencoder-based class representations. The impressive performances on heterophilic benchmarks reaffirm the superiority of our approach. We also study the effects of rewiring special edges like self-loops and parallel edges. In the fourth chapter of the thesis, we investigate the effects of the addition of self-loops and parallel edges on the eigenvalues of the graph Laplacian. Empirically, we observe that the gradual addition of self-loops or parallel edges generates performance trends (either increasing or decreasing) on the heterophilic graphs. This work offers insights into the graph spectrum based on the observed performance trends, bypassing the need to execute expensive eigenvalue decomposition. The deep GNNs also suffer from Oversquashing, an information bottleneck arises due to the requirement of storing exponentially growing information into fixed capacity channels. In the fifth chapter of the thesis, we propose asynchronous message passing to utilize fixed-capacity channels in a time-dependent access. This prevents the capacity constraints and ultimately overcomes oversquashing. We achieved commendable performances on the REDDIT-BINARY and Peptides-struct datasets. To mitigate both oversmoothing and oversquashing, Graph Transformers (GTs) come into the scenario to enable pair-wise message passing across the network. Precisely, GT incorporates structural information of the underlying graph datasets via positional encodings. In the sixth chapter of the thesis, we designed a novel and efficient positional encoding that is learnable and maps the encodings into hyperbolic spaces. Our positional encodings are expressive and efficiently capture hierarchical structures embedded in the molecular graphs, which is validated by extensive theoretical underpinnings. We further demonstrate that hyperbolic positional encodings, when added with features in final layers, diminish the effects of oversmoothing. We achieved superior performance on MNIST and OGBG-MOLHIV graphs by employing hyperbolic positional encodings. In the seventh chapter of the thesis, we shed light on the potential future research avenues and scope in the domain of GNN.
Essays on Monetary-Fiscal Interactions in Emerging Market and Developing Economies
(Indian Statistical Institute, 2025-07-17) Bahl, Ojasvita
This thesis contains three chapters on monetary-fiscal interactions in Emerging Market and Developing Economies. Governments in emerging markets and developing economies (EMDEs) frequently intervene in agricultural markets to stabilize food prices following adverse shocks. These interventions often take the form of large-scale food procurement and redistribution, which we define as a redistributive policy shock. This chapter examines the effects of such shocks on inflation and the distribution of consumption between rich and poor households. We develop a tractable two-sector, two-agent New Keynesian DSGE model and estimate its parameters for the Indian economy using Bayesian methods. Our findings reveal that under an inflation-targeting regime, consumer heterogeneity plays a crucial role in determining whether monetary policy responses to various shocks enhance or reduce aggregate welfare. The second chapter evaluates the welfare implications of redistributive policy shocks under alternative monetary policy regimes. Building on Chapter 1, which finds that redistributive policy shocks are inflationary and expansionary in terms of aggregate output, we assess how different monetary responses alter welfare outcomes. Following Schmitt-Grohe Uribe (2007), we compute consumptionequivalent welfare gains to compare the welfare cost of these shocks under the optimised simple monetary rule and the planner’s solution (Ramsey Optimal Monetary Policy). The optimal rule features no interest rate smoothing, a strong response to inflation, and a limited reaction to output. Our findings demonstrate the critical role of monetary policy in shaping the welfare impact of redistributive shocks. We further compare these welfare effects to those of an agricultural productivity shock and show that the steady-state level of redistribution significantly affects the relative costs of redistribution-driven fluctuations. We find that non-optimised rules lead to significantly higher welfare costs than optimised simple rules. In the third chapter, we study the interactions between informality, underdeveloped financial markets and fiscal consolidation by developing a two-sector, twoagent medium-scale NK-DSGE model that allows public expenditure and private consumption to be either substitutes or complements. While there is a large literature that tries to understand the effects of fiscal consolidation in AEs, there is a relatively small literature on fiscal consolidation in EMDEs. We find that greater informality dampens the reduction in public debt from a contractionary fiscal policy shock. We find tax-based shocks to exhibit greater decline in debt at the cost of a greater contraction in output than spending-based shocks. Our analysis suggests that a fiscal consolidation shock can be expansionary when private consumption and public spending exhibit moderately-high substitutability consistent with the literature on expansionary fiscal consolidations.