Privacy in Cloud Computing through Immersion-based Coding
Haleh Hayati, Nathan van de Wouw, Carlos Murguia

TL;DR
This paper introduces a novel immersion-based coding framework combining differential privacy and control theory to enable privacy-preserving cloud data processing without utility loss.
Contribution
It proposes a new synthesis framework that co-designs coding mechanisms, target algorithms, and decoding functions to ensure privacy and utility in cloud computing.
Findings
Achieves any desired differential privacy level without utility degradation.
Demonstrates effectiveness in optimization, learning algorithms, and control systems.
Provides a systematic approach to privacy-preserving data sharing in cloud environments.
Abstract
Cloud computing enables users to process and store data remotely on high-performance computers and servers by sharing data over the Internet. However, transferring data to clouds causes unavoidable privacy concerns. Here, we present a synthesis framework to design coding mechanisms that allow sharing and processing data in a privacy-preserving manner without sacrificing data utility and algorithmic performance. We consider the setup where the user aims to run an algorithm in the cloud using private data. The cloud then returns some data utility back to the user (utility refers to the service that the algorithm provides, e.g., classification, prediction, AI models, etc.). To avoid privacy concerns, the proposed scheme provides tools to co-design: 1) coding mechanisms to distort the original data and guarantee a prescribed differential privacy level; 2) an equivalent-but-different…
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Taxonomy
TopicsAdvanced Steganography and Watermarking Techniques
