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Fortunately, there are several *privacy-enhancing technologies (PETs)* that can help mitigate these risks. One of the most important is *differential privacy*. This technique adds noise to the data to protect individual privacy while still allowing for meaningful analysis. Then there is *federated learning*. This allows you to train AI models on decentralized data without directly sharing the raw data. The models are trained locally and then aggregated. *Homomorphic encryption* is also important. This allows you to perform computations on encrypted data without decrypting it pos+indonesia+career first. Also, *secure multi-party computation (SMPC)* is a technique that enables multiple parties to perform computations on their data without revealing the data to each other. Furthermore, *data masking* is also an important technique. This involves replacing sensitive information with a substitute that is not personally identifiable. Think of it like blurring out faces in a photo. And finally, *privacy-preserving data storage and access controls* are crucial. You need to use technologies like encryption and access controls to protect data stored in distributed systems.
