Breaking Data Silos: How GDI is Transforming Access to Geospatial Information in India
Authors: Bryan Paul Robert, Mahidhar Chellamani, Jyotirmoy Dutta Know more For years, some of India’s most valuable geospatial datasets remained scattered across government departments, research institutes, or private organizations. They held immense potential to transform logistics, strengthen climate resilience, and support smarter urban planning, but they remained difficult to access, buried in different formats and […]
Adaptive Self-Distillation for Minimizing Client Drift in Heterogeneous Federated Learning
Authors: M. Yashwanth, G. K. Nayak, A. Singh, Y. Simmhan, A. Chakraborty Know more Federated Learning (FL) is a machine learning paradigm that enables clients to jointly train a global model by aggregating the locally trained models without sharing any local training data. In practice, there can often be substantial heterogeneity (e.g., class imbalance) across […]
Privacy-Preserving Data Quality Assessment for Time-Series IoT Sensors
Authors: N. Chakraborty, A. Sharma, J. Dutta. H. D. Kumar Know more This paper proposes a novel framework for automated, objective, and privacy-preserving data quality assessment of time-series data from IoT sensors deployed in smart cities. We leverage custom, autonomously computable metrics that parameterise the temporal performance and adherence to a declarative schema document to […]
Automatable Data Quality Dimensions for Data Exchange: Formulation and Application
Authors: Debarun Sengupta, Anjula Gurtoo, Minnu Malieckal, Jyotirmoy Dutta Download PDF Large amounts of data get generated and applied in decision making to improve outcomes. However, quality of the data remains an issue as data gets generated from varied sources, in unspecified formats, and variables vary across different types of data. Identifying key quality dimensions […]
Ethics and Fair Use Framework for Privacy Preserving Data Sharing
Authors: Bita Afsharinia, Anjula Gurtoo, Jyotirmoy Dutta, Minnu Malieckal Download PDF The study aims to critically evaluate current privacy-preserving technologies and ethical frameworks in data sharing, identifying gaps and proposing a comprehensive, integrated ethical framework. Existing frameworks often fall short in integrating these two aspects effectively, particularly in the context of emerging technologies. The study […]
Privacy-Preserving Data Sharing: A Comprehensive Literature Review
Authors: Bita Afsharinia, Anjula Gurtoo, Jyotirmoy Dutta, Minnu Malieckal Download PDF This report provides an overview of the key concepts and techniques in privacy-preserving data analysis, emphasizing the importance of differential privacy, data anonymization, and data perturbation. The privacy-preserving principles of autonomy, justice, non-maleficence, beneficence, and explicability are critical for maintaining ethical standards in data […]
Data Governance: Some Pertinent Points for Discussion
Authors: Anjula Gurtoo, Jyotirmoy Dutta Download PDF The data governance panel during the 24 November 2023 Symposium on Data for Public Good at the Indian Institute of Science debated pertinent issues in the quest for a pragmatic policy framework for data governance. This document details and collates the views of different stakeholders.
Playbook for Data Quality Management for Geospatial Data
Authors: Minnu Malieckal, Anjula Gurtoo, Jyotirmoy Dutta, Linda Theres B, Sandeep P Download PDF This document is intended as a playbook for the geospatial sector to better understand what is meant by data, its use and how to manage its quality. The playbook details domains and datasets in geospatial sector, types of data, data formats, […]
Building a Privacy Web with SPIDEr – Secure Pipeline for Information De-Identification with End-to-End Encryption
Authors: N. Chakraborty, A. Tandon, K. Reddy, K. Kirpekar, B. Robert, H. Kumar, A. Venkatesh, A. Sharma Know more Data de-identification makes it possible to glean insights from data while preserving user privacy. The use of Trusted Execution Environments (TEEs) allow for the execution of de-identification applications on the cloud without the need for a […]
Optimal Tree-Based Mechanisms for Differentially Private Approximate CDFs
Authors: V. A. Rameshwar, A. Tandon, and A. Sharma Know more This paper considers the epsilon-differentially private (DP) release of an approximate cumulative distribution function (CDF) of the samples in a dataset. We assume that the true (approximate) CDF is obtained after lumping the data samples into a fixed number K of bins. In this […]