Machine Learning data should be private. It requires high computation and hence not scalable

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Secure Model Training: Zero-Knowledge Proofs can be used to ensure that the data used for training machine learning models remains private. Instead of sharing the raw data, parties can prove certain properties about their data without revealing the actual data itself. For example, they can prove that their data satisfies certain statistical properties necessary for training a model without disclosing the actual data points. Secure Model Evaluation: Zero-Knowledge Proofs can also be applied to ensure privacy during the evaluation of machine learning models. For example, if a model is hosted on a server and clients want to use the model to make predictions on their data without revealing their data to the server, Zero-Knowledge Proofs can be used. Clients can prove that their inputs are valid without revealing the inputs themselves. Privacy-Preserving Aggregation: In scenarios where multiple parties want to aggregate their data for training a model without revealing individual data points, Zero-Knowledge Proofs can be employed. Each party can prove certain properties of their data, such as the mean or sum, without disclosing the actual data points, and the proofs can be aggregated to obtain the result. Secure Multi-Party Computation (MPC): Zero-Knowledge Proofs can also be combined with Secure Multi-Party Computation techniques to enable privacy-preserving machine learning across multiple parties. MPC allows parties to jointly compute a function over their inputs while keeping those inputs private. Zero-Knowledge Proofs can be used within MPC protocols to ensure that each party's input is valid without revealing the input itself. Data Ownership Verification: Zero-Knowledge Proofs can be used to verify ownership of data without revealing the data itself. This can be useful in scenarios where parties want to prove that they possess certain data without disclosing the data content.

Secure Model Training: Zero-Knowledge Proofs can be used to ensure that the data used for training machine learning models remains private. Instead of sharing the raw data, parties can prove certain properties about their data without revealing the actual data itself. For example, they can prove that their data satisfies certain statistical properties necessary for training a model without disclosing the actual data points. Secure Model Evaluation: Zero-Knowledge Proofs can also be applied to ensure privacy during the evaluation of machine learning models. For example, if a model is hosted on a server and clients want to use the model to make predictions on their data without revealing their data to the server, Zero-Knowledge Proofs can be used. Clients can prove that their inputs are valid without revealing the inputs themselves. Privacy-Preserving Aggregation: In scenarios where multiple parties want to aggregate their data for training a model without revealing individual data points, Zero-Knowledge Proofs can be employed. Each party can prove certain properties of their data, such as the mean or sum, without disclosing the actual data points, and the proofs can be aggregated to obtain the result. Secure Multi-Party Computation (MPC): Zero-Knowledge Proofs can also be combined with Secure Multi-Party Computation techniques to enable privacy-preserving machine learning across multiple parties. MPC allows parties to jointly compute a function over their inputs while keeping those inputs private. Zero-Knowledge Proofs can be used within MPC protocols to ensure that each party's input is valid without revealing the input itself. Data Ownership Verification: Zero-Knowledge Proofs can be used to verify ownership of data without revealing the data itself. This can be useful in scenarios where parties want to prove that they possess certain data without disclosing the data content.

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