Privacy Challenges In Image Processing Applications

TL;DR

This paper reviews privacy challenges in image processing, highlighting techniques like differential privacy, MPC, and homomorphic encryption, and discusses future directions to balance privacy and utility in sensitive applications.

Contribution

It provides a comprehensive survey of emerging privacy-preserving techniques and identifies future research directions in privacy-aware image processing.

Findings

Differential privacy injects noise for privacy guarantees.

MPC enables collaborative analysis without exposing raw data.

Homomorphic encryption allows computations on encrypted images.

Abstract

As image processing systems proliferate, privacy concerns intensify given the sensitive personal information contained in images. This paper examines privacy challenges in image processing and surveys emerging privacy-preserving techniques including differential privacy, secure multiparty computation, homomorphic encryption, and anonymization. Key applications with heightened privacy risks include healthcare, where medical images contain patient health data, and surveillance systems that can enable unwarranted tracking. Differential privacy offers rigorous privacy guarantees by injecting controlled noise, while MPC facilitates collaborative analytics without exposing raw data inputs. Homomorphic encryption enables computations on encrypted data and anonymization directly removes identifying elements. However, balancing privacy protections and utility remains an open challenge. Promising future directions identified include quantum-resilient cryptography, federated learning, dedicated hardware, and conceptual innovations like privacy by design. Ultimately, a holistic effort combining technological innovations, ethical considerations, and policy frameworks is necessary to uphold the fundamental right to privacy as image processing capabilities continue advancing rapidly.