Differential privacy
Posted: Tue Feb 11, 2025 6:42 am
One of the problems with traditional methods of anonymizing data is that people often have little understanding of how well they protect privacy. The techniques described, which relate to managing the disclosure of statistics, are often dictated by intuition and empirical observations.
However, in a 2006 paper , Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam D. Smith provided a mathematical definition of the loss of privacy that results from releasing data from a statistical database. This relatively new approach makes the process of enforcing privacy in statistical databases more stringent. It is called differential privacy (or more precisely, ε-differential privacy). This algorithm inserts random data into a data set in a strictly mathematical way to protect privacy.
The data is obfuscated to the point that the query result is greece whatsapp data personally identifiable. The results will not be as accurate as the raw data. (How accurate depends on the methods used.) But other researchers have shown that very accurate statistics can be extracted from the database while still maintaining a high level of privacy.
Differential privacy remains an area of active research. But the technique is already being used. For example, the U.S. Census Bureau will use it to protect the 2020 census results.
Fully homomorphic encryption
Fully homomorphic encryption allows someone to perform complex processing on data without seeing it. It is essentially an extension of public key encryption. It was first mentioned shortly after the invention of the RSA cryptosystem.
This method requires very large computing resources and has not yet found widespread use. However, it will create an additional level of protection against data leakage when using public clouds or involving service providers in the analysis of data sets.
Confidential Computing Protocol
However, in a 2006 paper , Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam D. Smith provided a mathematical definition of the loss of privacy that results from releasing data from a statistical database. This relatively new approach makes the process of enforcing privacy in statistical databases more stringent. It is called differential privacy (or more precisely, ε-differential privacy). This algorithm inserts random data into a data set in a strictly mathematical way to protect privacy.
The data is obfuscated to the point that the query result is greece whatsapp data personally identifiable. The results will not be as accurate as the raw data. (How accurate depends on the methods used.) But other researchers have shown that very accurate statistics can be extracted from the database while still maintaining a high level of privacy.
Differential privacy remains an area of active research. But the technique is already being used. For example, the U.S. Census Bureau will use it to protect the 2020 census results.
Fully homomorphic encryption
Fully homomorphic encryption allows someone to perform complex processing on data without seeing it. It is essentially an extension of public key encryption. It was first mentioned shortly after the invention of the RSA cryptosystem.
This method requires very large computing resources and has not yet found widespread use. However, it will create an additional level of protection against data leakage when using public clouds or involving service providers in the analysis of data sets.
Confidential Computing Protocol