Abstract
Advancements in machine learning and data science deal with the collection of a tremendous amount of data for research and analysis, following which there is a growing awareness among a large number of users about their sensitive data, and hence privacy protection has seen significant growth. Differential privacy is one of the most popular techniques to ensure data protection. However, it has two major issues: first, utility-privacy tradeoff, where users are asked to choose between them; second, the real-time implementation of such a system on high-dimensional data is missing. In this work, we propose BUDS+, a novel differential privacy framework that achieves an impressive privacy budget of 0.03. It introduces iterative shuffling, embedding for data encoding, converger function into a novel comparison system to converge the privacy threshold among the aggregated differentially private and noisy reports to further minimize the attack model’s time.
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