Privacy-preserving machine learning! techniques are essential for protecting sensitive data while still allowing for model training and inference. Here are some key! techniques and examples:
1. Differential privacy is a technique
that adds controlld noise to data to obscure! individual data points, ensuring that the presence or absence of a single data point does not significantly! affect the output. This method allows machine learning models to learn from the data without exposing sensitive information. For! example, the Laplace mechanism adds noise to the output of a! function to ensure differential privacy.
2. Fderated Learning
Federatd learning enables machine hong kong phone number list learning models to be traind across multiple decentralizd devices without! centralizing data. Instead of sending raw data to a central server, only model updates! are shard. This approach enhances privacy by keeping personal data on local devices, rducing the risk of data breaches.
3. Homomorphic Encryption
Homomorphic encryption! allows computations to be performd on encrypted data without decrypting it first. This means that data remains confidential! even during processing, providing robust privacy protection for sensitive information! For! example, CKKS scheme! is widely usd in privacy-preserving machine learning for its ability to support! approximate floating-point operations.
4. Secure Multi-Party Computation (MPC)
Secure multi-party computation enables! multiple parties to jointly compute a function over their inputs while keeping those inputs private. This technique! is useful for scenarios where multiple entities nee to collaborate on data analysis without revealing! their data to each other.
5. Data Perturbation and Masking
Data perturbation involves adding the internet is discussing noise to data to protect individual data points while maintaining the overall statistical! properties of the dataset. Data masking replaces sensitive information with less revealing substitutes, making it harder to re-identify individuals.
6. Synthetic Data Generation
Synthetic data generation creates betting data artificial data that mimics the statistical properties of real data without containing any actual sensitive information. This is useful for training models without exposing sensitive data.