Re:Privacy Loss in Apple's Implementation of Differential Pr...
by tgragnato on Tuesday September 12, 2017 @04:56AM
(#15225816)
Anything collected might break privacy, because that information may be something sensitive. Differential privacy relies upon obfuscating reported data in a statistically meaningful way.
Accordingly, you need to analyse aggregate statistics only, add random inaccuracy and apply data binning, anonymise reports. And you need to calibrate the noise. The latter is what the paper seems to be mainly focused on.
> We call for Apple to make its implementation of privacy-preserving algorithms public and to make the rate of privacy loss fully trans-parent and tunable by the user.
"Calibrating Noise to Sensitivity in Private Data Analysis" is about the matter.
Accordingly, you need to analyse aggregate statistics only, add random inaccuracy and apply data binning, anonymise reports. And you need to calibrate the noise. The latter is what the paper seems to be mainly focused on.
> We call for Apple to make its implementation of privacy-preserving algorithms public and to make the rate of privacy loss fully trans-parent and tunable by the user.
"Calibrating Noise to Sensitivity in Private Data Analysis" is about the matter.
- https://www.microsoft.com/en-us/research/publication/calibra...
- https://link.springer.com/content/pdf/10.1007%2F11681878_14....