Author:
Diyora Vishal,Savani Nilesh
Abstract
Abstract
Differential privacy, a vital concept in data privacy protection, has seen various paradigms emerge, ranging from centralized to localized approaches. This research explores two intermediate models known as the shuffle and pan-private models. These models bridge the gap between central curation and local user-centric data randomization, each offering a distinct balance between privacy and statistical utility. We delve into the necessity for different trust levels in these models, considering both engineering and mathematical viewpoints. In addition, we present a comparative analysis of the two models to clarify their differences.
Publisher
Research Square Platform LLC
Reference19 articles.
1. Kashyap GS, Malik K, Wazir S, Khan R (2022) “Using Machine Learning to Quantify the Multimedia Risk Due to Fuzzing,” Multimedia Tools and Applications, vol. 81, no. 25, pp. 36685–36698, Oct. 10.1007/s11042-021-11558-9
2. Kashyap GS, Brownlee AEI, Phukan OC, Malik K, Wazir S (2023) “Roulette-Wheel Selection-Based PSO Algorithm for Solving the Vehicle Routing Problem with Time Windows,” Jun. Accessed: Jul. 04, 2023. [Online]. Available: https://arxiv.org/abs/2306.02308v1
3. An analysis of the robustness of UAV agriculture field coverage using multi-agent reinforcement learning;Marwah N;Int J Inform Technol (Singapore)
4. Wazir S, Kashyap GS, Malik K, Brownlee AEI (2023) Predicting the Infection Level of COVID-19 Virus Using Normal Distribution-Based Approximation Model and PSO. ” Springer, Cham, pp 75–91. 10.1007/978-3-031-33183-1_5
5. Kanojia M, Kamani P, Kashyap GS, Naz S, Wazir S, Chauhan A (2023) “Alternative Agriculture Land-Use Transformation Pathways by Partial-Equilibrium Agricultural Sector Model: A Mathematical Approach,” Aug. Accessed: Sep. 16, 2023. [Online]. Available: https://arxiv.org/abs/2308.11632v1