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Open AccessProceedings ArticleDOI

RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Response

TLDR
RAPPOR as discussed by the authors is a system for crowdsourcing statistics from end-user client software, anonymously, with strong privacy guarantees, allowing the forest of client data to be studied, without permitting the possibility of looking at individual trees.
Abstract
Randomized Aggregatable Privacy-Preserving Ordinal Response, or RAPPOR, is a technology for crowdsourcing statistics from end-user client software, anonymously, with strong privacy guarantees. In short, RAPPORs allow the forest of client data to be studied, without permitting the possibility of looking at individual trees. By applying randomized response in a novel manner, RAPPOR provides the mechanisms for such collection as well as for efficient, high-utility analysis of the collected data. In particular, RAPPOR permits statistics to be collected on the population of client-side strings with strong privacy guarantees for each client, and without linkability of their reports. This paper describes and motivates RAPPOR, details its differential-privacy and utility guarantees, discusses its practical deployment and properties in the face of different attack models, and, finally, gives results of its application to both synthetic and real-world data.

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Practical Secure Aggregation for Privacy Preserving Machine Learning.

TL;DR: This protocol allows a server to compute the sum of large, user-held data vectors from mobile devices in a secure manner, and can be used, for example, in a federated learning setting, to aggregate user-provided model updates for a deep neural network.
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Federated Learning with Differential Privacy: Algorithms and Performance Analysis

TL;DR: A novel framework based on the concept of differential privacy, in which artificial noise is added to parameters at the clients’ side before aggregating, namely, noising before model aggregation FL (NbAFL), is proposed and an optimal convergence bound is found that achieves the best convergence performance at a fixed privacy level.
Journal ArticleDOI

Securing Fog Computing for Internet of Things Applications: Challenges and Solutions

TL;DR: The architecture and features of fog computing are reviewed and critical roles of fog nodes are studied, including real-time services, transient storage, data dissemination and decentralized computation, which are expected to draw more attention and efforts into this new architecture.
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Extracting Training Data from Large Language Models

TL;DR: This paper demonstrates that in such settings, an adversary can perform a training data extraction attack to recover individual training examples by querying the language model, and finds that larger models are more vulnerable than smaller models.
References
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Book ChapterDOI

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Journal Article

Calibrating noise to sensitivity in private data analysis

TL;DR: The study is extended to general functions f, proving that privacy can be preserved by calibrating the standard deviation of the noise according to the sensitivity of the function f, which is the amount that any single argument to f can change its output.