Privacy considerations in Computer Vision - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Understanding Data Anonymization in Computer Vision

Which of the following best describes data anonymization in the context of computer vision?

A Encrypting image files so only authorized users can view them

B Removing or obscuring personal identifiers in images to prevent tracing back to individuals

C Collecting more images to improve model accuracy without restrictions

D Sharing raw images with third parties for faster processing

💡 Hint

Think about how to protect personal identity in images.

Data anonymization means hiding or removing personal details in images so people cannot be identified, which protects privacy.

❓ Model Choice

intermediate

Choosing a Privacy-Preserving Model

You want to build a face recognition system but must ensure user privacy by not storing raw images. Which model approach is best?

A Use a cloud service that stores all images unencrypted

B Upload all raw images to a central server for training

C Train a model locally on user devices and only send encrypted feature vectors to the server

D Collect images without user consent to increase dataset size

💡 Hint

Consider how to keep raw images private while still training a model.

Training locally and sending only encrypted features keeps raw images private and reduces privacy risks.

❓ Metrics

advanced

Evaluating Privacy Leakage

Which metric would best measure how much private information a computer vision model unintentionally reveals?

A Membership inference attack success rate

B Model accuracy on test images

C Training loss value

D Number of model parameters

💡 Hint

Think about attacks that try to find if data was used in training.

Membership inference attack success rate measures how well an attacker can tell if a specific image was in the training set, indicating privacy leakage.

🔧 Debug

advanced

Identifying Privacy Risks in Code

Given this code snippet for a face detection pipeline, which line introduces a privacy risk?

import cv2

image = cv2.imread('user_photo.jpg')
faces = face_detector.detect(image)
cv2.imwrite('detected_faces.jpg', faces)
upload_to_server('detected_faces.jpg')

A cv2.imwrite('detected_faces.jpg', faces)

B face_detector.detect(image)

C cv2.imread('user_photo.jpg')

D upload_to_server('detected_faces.jpg')

💡 Hint

Consider what data is sent outside the local system.

Uploading detected faces to a server can expose personal data if not anonymized or encrypted, creating a privacy risk.

🧠 Conceptual

expert

Balancing Model Utility and Privacy

Which technique best balances maintaining model accuracy while protecting privacy in computer vision?

A Differential privacy applied during model training

B Removing all personal data and training on random noise

C Sharing raw images with all collaborators

D Training on a very small dataset to avoid leaks

💡 Hint

Think about adding controlled noise to protect data but keep useful patterns.

Differential privacy adds noise during training to protect individual data points while preserving overall model performance.