News
Safe and Responsible Computing
Network Security and Privacy: At Security and Privacy Innovation Group (SPRING), we focus on three research thrust areas. Our first thrust involves developing advanced ML/AI-based intrusion detection systems that can detect advanced attacks in diverse cyber-physical system environments. By analyzing network traffic patterns, device behavior, and user interactions in smart home networks, automotive systems, drone communications, speech recognition systems, 5G networks, and edge computing infrastructures, we aim to detect anomalies indicative of malicious activity. Further, our second thrust involves developing efficient and secure authentication mechanisms for resource-constrained networked devices. By examining the constraints of specific systems, we aim to create lightweight cryptographic solutions that protect data integrity and authenticity without compromising system performance. Finally, our third thrust focuses on developing robust privacy-preserving techniques for sensitive data. We investigate methods to protect user data while enabling data authentication. Through these thrust areas, we aim to develop resilient cyber-physical systems that can withstand emerging threats, enable secure communication, and maintain data privacy while ensuring minimal impact on their energy efficiency and performance.
Lead faculty: https://www.cse.iitd.ernet.in/~viresh/
Federated Data Discovery: Data-driven decision making has become the backbone of modern innovation, powering advancements in science, business, and policy. Organizations and researchers rely on vast amounts of data to generate insights, train AI models, and drive strategic decisions. However, strict data regulations and privacy concerns often prevent centralization, making it difficult to discover and access valuable datasets. Laws such as the General Data Protection Regulation (GDPR) in Europe, India’s Digital Personal Data Protection Act (DPDP) impose strict controls on how data is stored, shared, and processed. In federated settings, where data remains distributed across multiple organizations, innovative search and discovery mechanisms are needed to enable access while ensuring compliance with these regulations. This creates an exciting research frontier for developing decentralized techniques, privacy-preserving search algorithms, and intelligent indexing systems.
Lead Faculty: Kaustubh Beedkar (https://web.iitd.ac.in/~kbeedkar/)
Compliant Data Systems: In today’s data driven world, organizations must not only manage vast amounts of data efficiently but also ensure that data processing and storage comply with stringent data regulations. Laws such as GDPR, India’s DPDP Act, and CCPA impose strict requirements on data access, retention, and processing, making compliance a critical aspect of modern data systems. However, existing data processing systems were not originally designed with compliance in mind, leading to challenges in enforcing regulator constraints at scale. To address this, compliance must become a first-class citizen in data management, deeply integrated into how data is stored, processed, and queried. This requires innovative approaches to specifying, enforcing, and optimizing compliance constraints at every state of the data life cycle. This field presents an exciting opportunity to rethink fundamental aspects of data processing systems, bridging the gap between regulatory requirements and system design.
Lead Faculty: Kaustubh Beedkar (https://web.iitd.ac.in/~kbeedkar/)