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.

翻译：随着图像处理系统的普及，鉴于图像中包含敏感个人信息，隐私问题日益凸显。本文探讨图像处理中的隐私挑战，并综述新兴的隐私保护技术，包括差分隐私、安全多方计算、同态加密和匿名化技术。隐私风险较高的关键应用领域包括医疗保健（医学图像包含患者健康数据）和可能实现不当追踪的监控系统。差分隐私通过注入受控噪声提供严格的隐私保证，而安全多方计算可在不暴露原始数据输入的情况下促进协作分析。同态加密支持对加密数据进行计算，匿名化技术则直接移除识别要素。然而，平衡隐私保护与数据效用仍是亟待解决的挑战。已确定的未来研究方向包括抗量子密码学、联邦学习、专用硬件以及隐私设计等概念创新。随着图像处理能力的快速发展，最终需要结合技术创新、伦理考量和政策框架的协同努力，以维护隐私这一基本权利。