By: Sunil Kumar Singh, CCET, Panjab University, Chandigarh, India; Akshat Gaurav, Ronin Institute, Montclair, USA
Artificial Intelligence (AI) has revolutionized various industries, from healthcare to finance, by enabling advanced data analysis and predictions. However, as AI applications handle sensitive data, ensuring privacy and security has become paramount. Cryptographic neural-network computation has emerged as a promising approach to protect sensitive information while preserving the benefits of AI. In this blog post, we will explore the advancements in privacy-preserving techniques through cryptographic neural-network computation and their role in securing AI applications.
Understanding Cryptographic Neural-Network Computation: Cryptographic neural-network computation refers to the integration of cryptographic protocols and techniques into the training and inference processes of neural networks. By incorporating cryptographic methods, we can enable secure and private computation, ensuring that sensitive data remains confidential while allowing the neural network to perform complex tasks and deliver accurate predictions. This approach offers a delicate balance between privacy and utility in AI applications.
Advancements in Privacy-Preserving Techniques:
- Homomorphic Encryption:
Homomorphic encryption plays a pivotal role in privacy-preserving computation. It enables computations to be performed on encrypted data without revealing the underlying information. Recent advancements in homomorphic encryption schemes, such as fully homomorphic encryption (FHE) and partially homomorphic encryption (PHE), have significantly improved the efficiency and feasibility of performing computations on encrypted data. These advancements enable privacy-preserving AI operations without compromising accuracy or utility.
Table 1: Advancements in Homomorphic Encryption
| Encryption Scheme | Description |
| Fully Homomorphic Encryption | Enables arbitrary computations on encrypted data |
| Partially Homomorphic Encryption | Supports specific computations on encrypted data |
| Homomorphic Encryption Libraries | Open-source libraries facilitating practical implementation |
- Secure Multi-Party Computation (MPC):
Secure multi-party computation (MPC) allows multiple parties to jointly perform computations on their respective private inputs without exposing sensitive information. In the context of cryptographic neural-network computation, MPC enables collaborative AI scenarios, such as training models on distributed datasets while preserving data privacy. Recent advancements in MPC protocols have improved the efficiency and scalability of secure computations, paving the way for practical implementations in privacy-preserving AI.
Table 2: Advancements in Secure Multi-Party Computation
| MPC Technique | Description |
| Yao’s Garbled Circuits | Secure computation using garbled circuits |
| Secret Sharing | Distributing computation across multiple parties |
| Homomorphic Secret Sharing | Combining homomorphic encryption and secret sharing |
- Differential Privacy:
Differential privacy is a technique that adds controlled noise to data to provide privacy guarantees while maintaining statistical utility. By applying differential privacy to neural-network computation, it becomes possible to protect individual data points from being exposed while obtaining accurate aggregated results. Recent advancements in differential privacy mechanisms, such as adaptive noise injection and privacy-preserving optimization algorithms, have contributed to the advancement of privacy-preserving AI.
Table 3: Advancements in Differential Privacy
| Differential Privacy Technique | Description |
| Adaptive Noise Injection | Dynamic noise addition for privacy preservation |
| Privacy-Preserving Optimization Algorithms | Balancing utility and privacy through optimization |
- Federated Learning:
Federated learning is a distributed learning approach that allows training models on local devices without transmitting sensitive data to a central server. By leveraging cryptographic techniques, such as secure aggregation protocols and encrypted model updates, federated learning ensures privacy during the learning process. Recent advancements in federated learning algorithms and protocols have improved the security and privacy guarantees for decentralized AI applications, fostering collaboration while safeguarding sensitive data.
Table 4: Advancements in Federated Learning
| Federated Learning Technique | Description |
| Secure Aggregation Protocols | Privacy-preserving aggregation of model updates |
| Encrypted Model Updates | Securing model updates during federated learning |
| Differential Privacy in Federated Learning | Protecting privacy during collaborative learning |
Balancing Privacy and Utility in Cryptographic Neural-Network Computation: Achieving the right balance between privacy and utility is crucial in cryptographic neural-network computation. While privacy-preserving techniques protect sensitive data, they may introduce additional computational overhead or impact the accuracy of AI models. Ongoing research focuses on optimizing cryptographic protocols, improving efficiency, and designing privacy-utility trade-offs to achieve the desired level of privacy without compromising AI performance.
Conclusion
Securing AI applications and protecting sensitive data are critical concerns in today’s digital landscape. Cryptographic neural-network computation, with its advancements in privacy-preserving techniques, offers a promising solution. By integrating cryptographic protocols like homomorphic encryption, secure multi-party computation, differential privacy, and federated learning, we can strike a balance between privacy and utility. As the field continues to evolve, cryptographic neural-network computation holds tremendous potential to empower AI while ensuring the confidentiality and privacy of sensitive information. Embracing these advancements will pave the way for a more secure and privacy-preserving future in AI-driven applications.
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Cite As:
Singh S.K., Gaurav A. (2023) Securing AI with Cryptographic Neural-Network Computation: Advancements in Privacy-Preserving Techniques, Insights2Techinfo, pp.1