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A Critical Study of Composition Algorithms in Differential Privacy
  • Manas Kumar Yogi,
  • * DrASNChakravarthy
Manas Kumar Yogi
Pragati Engineering College

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* DrASNChakravarthy
Jawaharlal Nehru Technological University Kakinada
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Differential privacy has emerged as a prominent framework for safeguarding individual privacy in the context of data analysis and statistical computations. With the proliferation of data-driven applications and the necessity to share sensitive information while preserving confidentiality, the study of composition algorithms in differential privacy becomes increasingly vital. This paper presents a critical examination of the various composition techniques employed to combine multiple privacy-preserving computations while upholding the integrity of the overarching privacy guarantees. The paper begins by outlining the fundamental principles of differential privacy and its significance in contemporary data-centric environments. It subsequently delves into a critical analysis of sequential composition, parallel composition, and advanced composition theorems. The inherent strengths and limitations of each technique are scrutinized; emphasizing their practical implications in preserving privacy across different scenarios. The study extends beyond basic composition strategies to explore intricate facets of composition algorithms. Post-processing and renewal-based composition methodologies are evaluated in the context of preserving privacy amidst evolving data landscapes. The paper also investigates adaptive data analysis and hierarchical composition, elucidating their roles in addressing complex privacy challenges that arise in multi-agent and multi-level environments. Through a meticulous survey of literature and practical implementations, this paper unveils the nuanced interplay between composition algorithms and differential privacy mechanisms. It highlights the need for judicious application of composition techniques while considering the dynamic interactions between privacy parameters, data characteristics, and adversarial behaviours. Furthermore, the paper underscores the significance of incorporating advanced composition theorems to yield more precise privacy bounds, providing a comprehensive understanding of the intricacies involved.