'Reconstruction Attacks: Examples of Invasion of Your Privacy Without Consent' by oblivious_AI datasecurity dataprivacy
In this post, we will look at some examples of reconstruction attacks, i.e., how one can reveal the most sensitive information about individuals from seemingly anonymous data. An example of how things might go wrong with aggregate statistics is revealing an average salary of 100 employees and then publishing an average of 101 after a new employee has joined. The task is even easier for an attacker who cleverly uses non-uniform sampling e.g.
If you don't follow a structured approach to data sharing, you've got a good chance of compromising the privacy of the data source. Many large companies and governments have made these mistakes, so let's talk about how you can avoid the same peril!Even if we think of ourselves as a needle in a haystack of 7.7 billion people globally, a range of studies has shown that very few data points suffice to uniquely or with high probability identify an individual.
However, sensitive information can be compromised even if the identifiers are not unique. It is well known that 87% of Americans can be uniquely identified just from their gender, birthday, and ZIP code [3]. To prevent such attacks, the commonly used method is to group and coarsen the identifiers by reporting only the age brackets, giving only the first three digits of ZIP codes, etc., resulting in quasi-identifiers. This is done in such a way as to guarantee k-anonymity.
Governor Bill Weld assured that the data had been fully anonymized. Sweeney paid $20 for the Cambridge, Massachusetts voter registration list, which also contained these 3 characteristics. Thus by cross-referencing the two databases, she identified Weld's entry in GIC and his medical records.