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Found 11 papers, 5 papers with code
Utility-Optimized Synthesis of Differentially Private Location Traces
Given a real trace dataset D, the differential privacy parameter epsilon controlling the strength of privacy protection, and the utility/error metric Err of interest; OptaTrace uses Bayesian optimization to optimize DPLTS such that the output error (measured in terms of given metric Err) is minimized while epsilon-differential privacy is satisfied.
Data Poisoning Attacks Against Federated Learning Systems
Federated learning (FL) is an emerging paradigm for distributed training of large-scale deep neural networks in which participants' data remains on their own devices with only model updates being shared with a central server.
Understanding Object Detection Through An Adversarial Lens
We demonstrate that the proposed framework can serve as a methodical benchmark for analyzing adversarial behaviors and risks in real-time object detection systems.
LDP-Fed: Federated Learning with Local Differential Privacy
However, in federated learning model parameter updates are collected iteratively from each participant and consist of high dimensional, continuous values with high precision (10s of digits after the decimal point), making existing LDP protocols inapplicable.
A Framework for Evaluating Gradient Leakage Attacks in Federated Learning
FL offers default client privacy by allowing clients to keep their sensitive data on local devices and to only share local training parameter updates with the federated server.
TOG: Targeted Adversarial Objectness Gradient Attacks on Real-time Object Detection Systems
The rapid growth of real-time huge data capturing has pushed the deep learning and data analytic computing to the edge systems.
Effects of Differential Privacy and Data Skewness on Membership Inference Vulnerability
Second, through MPLens, we highlight how the vulnerability of pre-trained models under membership inference attack is not uniform across all classes, particularly when the training data itself is skewed.
Secure and Utility-Aware Data Collection with Condensed Local Differential Privacy
In this paper, we address the small user population problem by introducing the concept of Condensed Local Differential Privacy (CLDP) as a specialization of LDP, and develop a suite of CLDP protocols that offer desirable statistical utility while preserving privacy.
Cryptography and Security Databases
Differentially Private Model Publishing for Deep Learning
However, when the training datasets are crowdsourced from individuals and contain sensitive information, the model parameters may encode private information and bear the risks of privacy leakage.
Adversarial Examples in Deep Learning: Characterization and Divergence
The burgeoning success of deep learning has raised the security and privacy concerns as more and more tasks are accompanied with sensitive data.
Towards Demystifying Membership Inference Attacks
Our empirical results additionally show that (1) using the type of target model under attack within the attack model may not increase attack effectiveness and (2) collaborative learning in federated systems exposes vulnerabilities to membership inference risks when the adversary is a participant in the federation.
Cryptography and Security
