Lavanya Elluri

Varun Mandalapu, Lavanya Elluri, Piyush Vyas et al.

Predicting crime using machine learning and deep learning techniques has gained considerable attention from researchers in recent years, focusing on identifying patterns and trends in crime occurrences. This review paper examines over 150 articles to explore the various machine learning and deep learning algorithms applied to predict crime. The study provides access to the datasets used for crime prediction by researchers and analyzes prominent approaches applied in machine learning and deep learning algorithms to predict crime, offering insights into different trends and factors related to criminal activities. Additionally, the paper highlights potential gaps and future directions that can enhance the accuracy of crime prediction. Finally, the comprehensive overview of research discussed in this paper on crime prediction using machine learning and deep learning approaches serves as a valuable reference for researchers in this field. By gaining a deeper understanding of crime prediction techniques, law enforcement agencies can develop strategies to prevent and respond to criminal activities more effectively.

Lavanya Elluri, Varun Mandalapu, Piyush Vyas et al.

Cybercrime is a growing threat to organizations and individuals worldwide, with criminals using sophisticated techniques to breach security systems and steal sensitive data. This paper aims to comprehensively survey the latest advancements in cybercrime prediction, highlighting the relevant research. For this purpose, we reviewed more than 150 research articles and discussed 50 most recent and appropriate ones. We start the review with some standard methods cybercriminals use and then focus on the latest machine and deep learning techniques, which detect anomalous behavior and identify potential threats. We also discuss transfer learning, which allows models trained on one dataset to be adapted for use on another dataset. We then focus on active and reinforcement learning as part of early-stage algorithmic research in cybercrime prediction. Finally, we discuss critical innovations, research gaps, and future research opportunities in Cybercrime prediction. This paper presents a holistic view of cutting-edge developments and publicly available datasets.

Anantaa Kotal, Lavanya Elluri, Deepti Gupta et al.

Big Data empowers the farming community with the information needed to optimize resource usage, increase productivity, and enhance the sustainability of agricultural practices. The use of Big Data in farming requires the collection and analysis of data from various sources such as sensors, satellites, and farmer surveys. While Big Data can provide the farming community with valuable insights and improve efficiency, there is significant concern regarding the security of this data as well as the privacy of the participants. Privacy regulations, such as the EU GDPR, the EU Code of Conduct on agricultural data sharing by contractual agreement, and the proposed EU AI law, have been created to address the issue of data privacy and provide specific guidelines on when and how data can be shared between organizations. To make confidential agricultural data widely available for Big Data analysis without violating the privacy of the data subjects, we consider privacy-preserving methods of data sharing in agriculture. Deep learning-based synthetic data generation has been proposed for privacy-preserving data sharing. However, there is a lack of compliance with documented data privacy policies in such privacy-preserving efforts. In this study, we propose a novel framework for enforcing privacy policy rules in privacy-preserving data generation algorithms. We explore several available agricultural codes of conduct, extract knowledge related to the privacy constraints in data, and use the extracted knowledge to define privacy bounds in a privacy-preserving generative model. We use our framework to generate synthetic agricultural data and present experimental results that demonstrate the utility of the synthetic dataset in downstream tasks. We also show that our framework can evade potential threats and secure data based on applicable regulatory policy rules.

Jeremiah L. Webb, Laxima Niure Kandel, Deepti Gupta et al.

Cryptographic operations are an essential component of cloud security architectures; their comprehensive performance characterization across different cloud services, hardware architectures, and programming language implementations remains unknown. Specifically, healthcare IoT devices are highly vulnerable and frequently targeted, yet the cryptographic performance trade offs in their cloud security architectures remain poorly understood. This research presents an extensive microbenchmark study evaluating the performance of core cryptographic workloads, including SHA HMAC generation, AES encryption, decryption, Elliptic Curve Cryptography (ECC) signature generation and verification, and RSA encryption, decryption, across Function as a Service (FaaS) integrated with Key Management Services (KMS) from Amazon Web Services (AWS) and Microsoft Azure. We evaluate FaaS platforms using Elastic Compute Cloud (EC2) instances and Azure Virtual Machines, specifically using burst optimized instance types to analyze performance under typical cloud workload patterns. The benchmark encompasses a comprehensive multi dimensional analysis spanning two CPU architectures (x86 64 and Arm64), six widely adopted programming languages (Rust, Go, Python, Java, C#, and TypeScript), multiple memory allocation configurations, and diverse instance types to capture the complex interplay between these factors. This study identifies optimal configurations for cryptographic workloads in FaaS environments, improving performance and cost efficiency while enabling secure and timely data protection for healthcare IoT applications.

Leon Garza, Lavanya Elluri, Anantaa Kotal et al.

Data protection and privacy is becoming increasingly crucial in the digital era. Numerous companies depend on third-party vendors and service providers to carry out critical functions within their operations, encompassing tasks such as data handling and storage. However, this reliance introduces potential vulnerabilities, as these vendors' security measures and practices may not always align with the standards expected by regulatory bodies. Businesses are required, often under the penalty of law, to ensure compliance with the evolving regulatory rules. Interpreting and implementing these regulations pose challenges due to their complexity. Regulatory documents are extensive, demanding significant effort for interpretation, while vendor-drafted privacy policies often lack the detail required for full legal compliance, leading to ambiguity. To ensure a concise interpretation of the regulatory requirements and compliance of organizational privacy policy with said regulations, we propose a Large Language Model (LLM) and Semantic Web based approach for privacy compliance. In this paper, we develop the novel Privacy Policy Compliance Verification Knowledge Graph, PrivComp-KG. It is designed to efficiently store and retrieve comprehensive information concerning privacy policies, regulatory frameworks, and domain-specific knowledge pertaining to the legal landscape of privacy. Using Retrieval Augmented Generation, we identify the relevant sections in a privacy policy with corresponding regulatory rules. This information about individual privacy policies is populated into the PrivComp-KG. Combining this with the domain context and rules, the PrivComp-KG can be queried to check for compliance with privacy policies by each vendor against relevant policy regulations. We demonstrate the relevance of the PrivComp-KG, by verifying compliance of privacy policy documents for various organizations.

Kanchon Gharami, Quazi Sarwar Muhtaseem, Deepti Gupta et al.

The development of robust transliteration techniques to enhance the effectiveness of transforming Romanized scripts into native scripts is crucial for Natural Language Processing tasks, including sentiment analysis, speech recognition, information retrieval, and intelligent personal assistants. Despite significant advancements, state-of-the-art multilingual models still face challenges in handling Romanized script, where the Roman alphabet is adopted to represent the phonetic structure of diverse languages. Within the South Asian context, where the use of Romanized script for Indo-Aryan languages is widespread across social media and digital communication platforms, such usage continues to pose significant challenges for cutting-edge multilingual models. While a limited number of transliteration datasets and models are available for Indo-Aryan languages, they generally lack sufficient diversity in pronunciation and spelling variations, adequate code-mixed data for large language model (LLM) training, and low-resource adaptation. To address this research gap, we introduce a novel transliteration dataset for two popular Indo-Aryan languages, Hindi and Bengali, which are ranked as the 3rd and 7th most spoken languages worldwide. Our dataset comprises nearly 1.8 million Hindi and 1 million Bengali transliteration pairs. In addition to that, we pre-train a custom multilingual seq2seq LLM based on Marian architecture using the developed dataset. Experimental results demonstrate significant improvements compared to existing relevant models in terms of BLEU and CER metrics.