Posthoc privacy guarantees for collaborative inference with modified Propose-Test-Release
Cloud-based machine learning inference is an emerging paradigm where users query by sending their data through a service provider who runs an ML model on that data and returns back the answer. Due to increased concerns over data privacy, recent works have proposed Collaborative Inference (CI) to learn a privacy-preserving encoding of sensitive user data before it is shared with an untrusted service provider. Existing works so far evaluate the privacy of these encodings through empirical reconstruction attacks. In this work, we develop a new framework that provides formal privacy guarantees for an arbitrarily trained neural network by linking its local Lipschitz constant with its local sensitivity. To guarantee privacy using local sensitivity, we extend the Propose-Test-Release (PTR) framework to make it tractable for neural network queries. We verify the efficacy of our framework experimentally on real-world datasets and elucidate the role of Adversarial Representation Learning (ARL) in improving the privacy-utility trade-off.
Code (0)
등록된 구현이 없습니다.
Methods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Collaborative Inference over Wireless Channels with Feature Differential Privacy
Collaborative inference among multiple wireless edge devices has the potential to significantly enhance Artificial Intelligence (AI) applications, particularly for sensing and computer vision. This approach typically inv…
Collaborative InferencePrivacy PreservingPRICURE: Privacy-Preserving Collaborative Inference in a Multi-Party Setting
When multiple parties that deal with private data aim for a collaborative prediction task such as medical image classification, they are often constrained by data protection regulations and lack of trust among collaborat…
Collaborative Inferenceimage-classificationImage ClassificationInference Attack+4Private Collaborative Edge Inference via Over-the-Air Computation
We consider collaborative inference at the wireless edge, where each client's model is trained independently on its local dataset. Clients are queried in parallel to make an accurate decision collaboratively. In addition…
Collaborative InferenceLeveraging Membership Inference Attacks for Privacy Measurement in Federated Learning for Remote Sensing Images
Federated Learning (FL) enables collaborative model training while keeping training data localized, allowing us to preserve privacy in various domains including remote sensing. However, recent studies show that FL models…
Remote Sensing Image ClassificationScene ClassificationFederated LearningPATROL: Privacy-Oriented Pruning for Collaborative Inference Against Model Inversion Attacks
Collaborative inference has been a promising solution to enable resource-constrained edge devices to perform inference using state-of-the-art deep neural networks (DNNs). In collaborative inference, the edge device first…
Collaborative InferenceVehicle Re-Identification