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Advancing Differential Privacy: Where We Are Now and Future Directions for Real-World Deployment

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arxiv 2304.06929 v2 pith:AKALWFOV submitted 2023-04-14 cs.CR

classification cs.CR
keywords privacyarticledifferentialadvancingchallengesdeploymentdesigndirections
verification ladder T0 review T1 audit T2 compute T3 formal
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In this article, we present a detailed review of current practices and state-of-the-art methodologies in the field of differential privacy (DP), with a focus of advancing DP's deployment in real-world applications. Key points and high-level contents of the article were originated from the discussions from "Differential Privacy (DP): Challenges Towards the Next Frontier," a workshop held in July 2022 with experts from industry, academia, and the public sector seeking answers to broad questions pertaining to privacy and its implications in the design of industry-grade systems. This article aims to provide a reference point for the algorithmic and design decisions within the realm of privacy, highlighting important challenges and potential research directions. Covering a wide spectrum of topics, this article delves into the infrastructure needs for designing private systems, methods for achieving better privacy/utility trade-offs, performing privacy attacks and auditing, as well as communicating privacy with broader audiences and stakeholders.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Gaze3P: Gaze-Based Prediction of User-Perceived Privacy

    cs.HC 2025-07 conditional novelty 6.0 of 10

    Gaze patterns predict users' perceived privacy ratings with moderate accuracy, and the ratings can be mapped to differential privacy noise levels that improve utility over static and random baselines.

  2. Inclusive Federated Learning Through Compliance-Weighted Noise Allocation in Healthcare AI

    cs.LG 2025-05 reject novelty 4.0 of 10

    Compliance-weighted noise allocation in federated healthcare learning claims no accuracy loss versus uniform noise, but its differential privacy guarantee applies only to the aggregator dataset, not client data.

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