Edge AI Security: Safeguarding Models and Data Quiz Quiz

Explore fundamental concepts of Edge AI security with this quiz designed to assess your knowledge of protecting AI models and sensitive data at the edge. Learn about risks, best practices, and key techniques used to enhance privacy and secure edge computing environments.

1. Physical Security in Edge AI

   Why is physical security important for edge AI devices deployed in public locations like train stations?

   1. To improve battery life in challenging weather
   2. To speed up data processing by optimizing hardware
   3. To enhance device decoration
   4. To prevent unauthorized access or tampering with the device and its data

   Explanation: Physical security is crucial because if an attacker physically accesses an edge AI device, they may steal, alter, or tamper with sensitive models and data. While speeding up processing, battery life, and aesthetics are device concerns, only physical security directly mitigates risks from unauthorized access. The other options do not address the risk of someone physically compromising the device.

2. Data Encryption on Edge Devices

   What is a primary benefit of encrypting data stored on edge devices, such as local surveillance cameras?

   1. Speeds up data transmission over local networks
   2. Prevents data from being read by unauthorized users if the device is lost or stolen
   3. Automatically deletes old data after processing
   4. Ensures data is always backed up to the cloud

   Explanation: Encryption protects sensitive data by making it unreadable to people without the key, even if the device is compromised. Cloud backup, faster transmission, and automatic deletion are not functions provided directly by data encryption. Only encryption specifically addresses confidentiality when devices are lost or stolen.

3. Model Theft Risk

   Which security threat describes attackers extracting and copying proprietary AI models from edge devices?

   1. Packet sniffing
   2. Model theft
   3. Denial of service
   4. Data spoofing

   Explanation: Model theft refers to attackers gaining access to and stealing AI models, which can result in loss of intellectual property and competitive advantage. Data spoofing involves falsifying input, denial of service disrupts availability, and packet sniffing monitors network traffic. None of these capture the threat of extracting models directly.

4. Privacy Risks in Edge AI

   What privacy risk arises when edge AI cameras process personal images without proper safeguards?

   1. Unauthorized exposure of sensitive information
   2. Increased device weight
   3. Longer battery charging times
   4. Slower device updates

   Explanation: Processing personal data at the edge without safeguards can lead to unauthorized exposure, violating privacy. Device update speed, weight, and charging times are not related to privacy risks. Only unwanted exposure of personal data directly results from inadequate privacy protection.

5. Adversarial Attacks Example

   What is an adversarial attack in the context of edge AI, for example, confusing a classifier with altered input images?

   1. Upgrading device firmware regularly
   2. Sharing training data across devices
   3. Increasing the resolution of camera images
   4. Intentionally modifying inputs to deceive the AI model

   Explanation: Adversarial attacks exploit small, deliberate changes in input data to manipulate AI predictions. Increasing resolution, data sharing, and firmware updates do not intentionally trick the AI. Only the correct option describes direct manipulation aimed at the AI's decision-making.

6. Secure Model Updates

   Why should model updates for edge AI devices be delivered using secure communication channels?

   1. To simplify user interfaces
   2. To reduce the cost of software maintenance
   3. To enhance the brightness of device screens
   4. To prevent attackers from introducing malicious or altered models

   Explanation: Secure channels ensure model updates are genuine and not tampered with during delivery, preventing attackers from replacing them with harmful versions. Cost savings, screen brightness, and simpler interfaces are unrelated to secure delivery of model updates. The other options do not address the risk of malicious interference.

7. Data Minimization

   How does data minimization help protect privacy on edge AI systems processing health data?

   1. It speeds up the AI model's training process
   2. It improves internet connectivity
   3. It increases the device's storage space
   4. It limits the collection and storage of personal information to what is strictly necessary

   Explanation: By minimizing the amount and type of data processed, data minimization reduces exposure and risk of privacy breaches. Training speed, storage space, and connectivity are not directly affected by limiting data collection. Only limiting data use addresses privacy protection.

8. Differential Privacy

   Which technique adds random noise to edge AI outputs to prevent the identification of individuals in collected data?

   1. Transfer learning
   2. Federated averaging
   3. Pruning
   4. Differential privacy

   Explanation: Differential privacy introduces statistical noise to outputs, protecting individual privacy during data analysis. Transfer learning involves reusing models, pruning removes unnecessary model parts, and federated averaging aggregates model updates. Only differential privacy is designed to shield identities through output modification.

9. Model Obfuscation Purpose

   What is the main purpose of using model obfuscation techniques for AI models deployed on edge devices?

   1. To speed up device boot times
   2. To improve display resolution
   3. To make it harder for attackers to reverse-engineer the AI model
   4. To reduce electricity consumption

   Explanation: Obfuscation disguises the internal structure of a model, deterring attackers from analyzing or stealing it. It does not affect device boot times, display quality, or energy use. The sole aim is to complicate attempts at reverse engineering or unauthorized retrieval.

10. Role of Access Control

    Why is access control critical for edge AI systems managing financial transactions?

    1. To make transactions go through faster
    2. To lower hardware costs
    3. To ensure only authorized users and services can interact with sensitive functions
    4. To increase screen refresh rates

    Explanation: Access control restricts operations to valid users, guarding against fraud and unauthorized actions on financial data. Transaction speed, hardware costs, and screen features are unrelated to the goal of access control. Only restricting access protects system integrity and sensitive functions.