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Incorporating Differential Privacy Techniques into Neural Networks Tim Giants Department of Computer Science Stanford University tgianit@stanford.eduAbstract Differential privacy is a formalism that
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Preserving differential privacy refers to the practice of protecting sensitive information in statistical databases or data analysis while still allowing accurate analysis and results to be obtained.
Organizations or individuals who collect, store, or analyze sensitive data are required to implement preserving differential privacy techniques.
Preserving differential privacy can be achieved through techniques such as adding noise to the data, conducting randomized response surveys, or using differential privacy algorithms.
The purpose of preserving differential privacy is to ensure that individuals' sensitive information is protected and not disclosed in statistical analysis or data sharing.
Information such as the type of data collected, the techniques used for preserving differential privacy, and the results of any privacy analysis conducted must be reported.
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