登录查看更多内容

What are the most effective ways to secure AI models in edge computing?

由人工智能和领英社区提供技术支持

Edge computing is a paradigm that enables AI models to run closer to the data sources, such as sensors, cameras, or mobile devices. This can improve performance, latency, bandwidth, and privacy of AI applications. However, edge computing also poses new challenges for securing AI models from malicious attacks, such as data poisoning, model stealing, or adversarial examples. In this article, you will learn about some of the most effective ways to protect your AI models in edge environments.

此文章中的业界达人

由社区从 6 条内容中精选。了解更多

1 Encrypt Data and Models

One of the simplest and most common ways to secure AI models in edge computing is to encrypt the data and the models before transmitting or storing them. Encryption can prevent unauthorized access, modification, or leakage of sensitive information. However, encryption also introduces some overhead and complexity, such as key management, decryption, and compatibility. Therefore, you need to choose the appropriate encryption scheme and level for your edge scenario, such as symmetric or asymmetric, homomorphic or non-homomorphic, or lightweight or robust.

添加您的观点

Sergio Ortega

Business Development and Sales Lead @ AWS | Generative AI | AI |Machine Learning | Business growth| Global Strategy
举报内容
Balancing security protections with performance constraints is key when securing edge AI. Lightweight ciphers like AES or chacha20 provide encryption with minimal overhead. Hybrid encryption uses asymmetric methods to establish keys then faster symmetric encryption for data transfers. Homomorphic encryption allows computation on encrypted data - partially homomorphic supporting either addition or multiplication has reasonable run-time penalties. Fully homomorphic permits arbitrary operations but slows processing considerably. Overall, combining high-speed and strong encryption tuned for edge resources through defense-in-depth best protects sensitive edge AI assets and pipelines.

已翻译

赞
Claudio Palmieri

Lawyer, Senior Legal Consultant - theinnovationlawyers.eu | EU law & policies. IT & new technologies. Compliance & Regulation
举报内容
The most effective ways to secure AI models in edge computing for points: - establishing solid authorization and authentication controls/access policies, included Secure Access Service Edge (Sase policy); - data encryption; - early threat detection; - vpn and firewall use; - schedule security patch updates; - Automated monitoring tools.

已翻译

赞
Timo Laine

AI Consultant | Results driven hands-on leader | Generative AI | Data Science | Machine Learning | Certified in 8xAzure, 4xAWS, 4xGoogle, 4xOracle, 4xNVIDIA | Passion for learning | PhD
举报内容
Security is important and always an issue. There is no one solution that solves all. Security is like an onion with multiple layers of protection. When an attacker attacks, one layer can be bypassed, but then comes the next layer. The attacker peels one layer off the onion and now it depends on the number of protections and layers, whether he/she can get to the core. If there are enough layers, the work is hard and complicated, the attacker might give up and think that there are other systems that are easier to break.

已翻译

赞

2 Use Trusted Execution Environments

Another way to secure AI models in edge computing is to use trusted execution environments (TEEs), which are hardware or software components that isolate and protect the execution of code and data from external interference. TEEs can ensure the integrity, confidentiality, and authenticity of AI models and data in edge devices, such as Intel SGX, ARM TrustZone, or Secure Enclaves. However, TEEs also have some limitations, such as scalability, compatibility, and vulnerability to side-channel attacks. Therefore, you need to evaluate the trade-offs and risks of using TEEs for your edge scenario.

添加您的观点

Timo Laine

AI Consultant | Results driven hands-on leader | Generative AI | Data Science | Machine Learning | Certified in 8xAzure, 4xAWS, 4xGoogle, 4xOracle, 4xNVIDIA | Passion for learning | PhD
举报内容
My experience is that the use of TEE depends on the use case and the criticality of the business. Technology and solutions must be the right size for the need.

已翻译

赞

3 Apply Differential Privacy

A third way to secure AI models in edge computing is to apply differential privacy, which is a technique that adds controlled noise to data or models to preserve the privacy of individual records or features. Differential privacy can prevent inference or re-identification attacks on AI models and data in edge devices, such as federated learning, collaborative filtering, or recommender systems. However, differential privacy also has some drawbacks, such as accuracy loss, parameter tuning, and utility degradation. Therefore, you need to balance the privacy and utility of your AI models and data in edge scenarios.

添加您的观点

Timo Laine

AI Consultant | Results driven hands-on leader | Generative AI | Data Science | Machine Learning | Certified in 8xAzure, 4xAWS, 4xGoogle, 4xOracle, 4xNVIDIA | Passion for learning | PhD
举报内容
Data quality is always a problem in AI models, and most of the time, effort and cost goes to data engineering work. The algorithms themselves are almost all equally good and if the data is right, one or two algorithms are enough to give accurate results. Therefore I prefer to have less data which is accurate than many features which have noise.

已翻译

赞

4 Implement Secure Aggregation

A fourth way to secure AI models in edge computing is to implement secure aggregation, which is a method that combines the outputs of multiple edge devices without revealing their individual inputs or models. Secure aggregation can enhance the privacy and efficiency of AI models and data in edge networks, such as distributed learning, consensus algorithms, or aggregation functions. However, secure aggregation also has some challenges, such as communication overhead, fault tolerance, and collusion resistance. Therefore, you need to design and optimize your secure aggregation protocol for your edge scenario.

添加您的观点

5 Adopt Adversarial Defense

A fifth way to secure AI models in edge computing is to adopt adversarial defense, which is a strategy that detects or mitigates the impact of adversarial examples, which are maliciously crafted inputs that can fool or degrade AI models. Adversarial defense can improve the robustness and reliability of AI models and data in edge devices, such as image recognition, natural language processing, or speech recognition. However, adversarial defense also has some issues, such as computational cost, generalization, and attack diversity. Therefore, you need to test and update your adversarial defense mechanism for your edge scenario.

添加您的观点

Javier Hernández

Azure Data & AI Specialist at Microsoft
举报内容
It's critical to consider that the attacks or hacks could come from other devices or users inside the company, so reduce the number of the devices or networks with access to the data or the models. Also need to put a first layer that could prevent DDoS attacks or malicious data inside the requests

已翻译

赞

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

添加您的观点

Computer Engineering

+ 关注

给文章评分

我们借助人工智能创建了此文章。您认为这篇文章怎么样？

很棒不太好

举报此文章

查看全部

What are the most effective ways to secure AI models in edge computing?

1

2

3

4

5

6

1 Encrypt Data and Models

2 Use Trusted Execution Environments

3 Apply Differential Privacy

4 Implement Secure Aggregation

5 Adopt Adversarial Defense

6 Here’s what else to consider

Computer Engineering

给文章评分

感谢您的反馈

更多Computer Engineering相关文章

更多相关阅读内容

What are the most effective ways to secure AI models in edge computing?

1

2

3

4

5

6

1 Encrypt Data and Models

2 Use Trusted Execution Environments

3 Apply Differential Privacy

4 Implement Secure Aggregation

5 Adopt Adversarial Defense

6 Here’s what else to consider

Computer Engineering

给文章评分

感谢您的反馈

查看其他技能