The framework of differential privacy protects an individual's privacy while publishing query responses on congregated data. In this work, a new noise addition mechanism for differential privacy is introduced where the noise added is sampled from a hybrid density that resembles Laplace in the centre and Gaussian in the tail. With a sharper centre and light, sub-Gaussian tail, this density has the best characteristics of both distributions. We theoretically analyze the proposed mechanism, and we derive the necessary and sufficient condition in one dimension and a sufficient condition in higher dimensions for the mechanism to guarantee approximate differential privacy. Numerical simulations corroborate the efficacy of the proposed mechanism compared to other existing mechanisms in achieving a better trade-off between privacy and accuracy.
Prof. Sheetal Kalyani, IIT Madras
Sheetal Kalyani received the B.E. degree in electronics and communication engineering from Sardar Patel University, Gujarat, India, in 2002, and the Ph.D. degree in electrical engineering from the Indian Institute of Technology Madras, India, in 2008. She was a Senior Research Engineer with the Centre of Excellence in Wireless Technology, Chennai, India, from 2008 to 2012. She is currently a Professor with the Department of Electrical Engineering, IIT Madras. Her current research interests include differential privacy, extreme value theory, generalized fading models, hypergeometric functions, performance analysis of wireless systems/networks, compressed sensing, machine learning, and deep learning for wireless applications.