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Techniques

Differential privacy

NOT ON THE CURRENT EDITION
This blip is not on the current edition of the radar. If it was on one of the last few editions it is likely that it is still relevant. If the blip is older it might no longer be relevant and our assessment might be different today. Unfortunately, we simply don't have the bandwidth to continuously review blips from previous editions of the radarUnderstand more
Mar 2017
assess?

It has long been known that "anonymized" bulk data sets can reveal information about individuals, especially when multiple data sets are cross-referenced together. With increasing concern over personal privacy, some companies—including Apple and Google—are turning to differential privacy techniques in order to improve individual privacy while retaining the ability to perform useful analytics on large numbers of users. Differential privacy is a cryptographic technique that attempts to maximize the accuracy of statistical queries from a database while minimizing the chances of identifying its records. These results can be achieved by introducing a low amount of "noise" to the data, but it's important to note that this is an ongoing research area. Apple has announced plans to incorporate differential privacy into its products—and we wholeheartedly applaud its commitment to customers' privacy—but the usual Apple secrecy has left some security experts scratching their heads. We continue to recommend Datensparsamkeit as an alternative approach: simply storing the minimum data you actually need will achieve better privacy results in most cases.

Nov 2016
assess?

It has long been known that "anonymized" bulk data sets can reveal information about individuals, especially when multiple data sets are cross-referenced together. With increasing concern over personal privacy, some companies—including Apple and Google—are turning to differential privacy techniques in order to improve individual privacy while retaining the ability to perform useful analytics on large numbers of users. Differential privacy is a cryptographic technique that attempts to maximize the accuracy of statistical queries from a database while minimizing the chances of identifying its records. These results can be achieved by introducing a low amount of "noise" to the data, but it’s important to note that this is an ongoing research area. Apple has announced plans to incorporate differential privacy into its products—and we wholeheartedly applaud its commitment to customers' privacy—but the usual Apple secrecy has left some security experts scratching their heads. We continue to recommend Datensparsamkeit as an alternative approach: simply storing the minimum data you actually need will achieve better privacy results in most cases.