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People Want Data Privacy but Don't Always Know What They're Getting - GovTech

People Want Data Privacy but Don't Always Know What They're Getting - GovTech | The Marteq Alert |
Differential privacy can be used to protect everyone’s personal data while gleaning useful information from it. Differential privacy disguises individuals’ information by randomly changing the lists of places they have visited, possibly by removing some locations and adding others. These introduced errors make it virtually impossible to compare people’s information and use the process of elimination to determine someone’s identity. Importantly, these random changes are small enough to ensure that the summary statistics – in this case, the most popular places – are accurate.

In practice, differential privacy isn’t perfect. The randomization process must be calibrated carefully. Too much randomness will make the summary statistics inaccurate. Too little will leave people vulnerable to being identified. Also, if the randomization takes place after everyone’s unaltered data has been collected, as is common in some versions of differential privacy, hackers may still be able to get at the original data.
CYDigital/'s insight:

Again, it starts with the individual unknowingly giving up their data, which inherently runs up against emerging legislation. 


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How statistical noise is protecting your data privacy | Microsoft

How statistical noise is protecting your data privacy | Microsoft | The Marteq Alert |
Differential privacy is a technology that allows the collection and sharing of data while safeguarding individual identities from being revealed. Other privacy techniques can be limiting and can result in sensitive information, such as bank details, becoming discoverable.

Differential privacy introduces statistical noise – slight alterations – to mask datasets. The noise hides identifiable characteristics of individuals, ensuring that the privacy of personal information is protected, but it’s small enough to not materially impact the accuracy of the answers extracted by analysts and researchers. This precisely calculated noise can be added at the point of data collection or when the data is analyzed.

Before queries are permitted, a privacy “budget” is created, which sets limits on the amount of information that can be taken from the data. Each time a query is asked of the data, the amount of information revealed is deducted from the overall budget available. Once that budget has been used up and further information would then risk personal privacy being compromised, additional queries are prevented. It’s effectively an automatic shut-off that prevents the system from revealing too much information.
CYDigital/'s insight:

The toolset is available from Microsoft. CT for the link.


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