Yes. This general problem is known as "k-anonymity". It's worth everyone who works with any sort of personally-identifiable data to read the original paper because the framework they identified is still really helpful for thinking about these issues. https://dataprivacylab.org/dataprivacy/projects/kanonymity/p...
8 hours in fuzzy spot one, 8 hours in fuzzy spot two, fitting some gaussian distribution, and then a few points at other times, and boom, not anonymous at all.
Indeed, I am pretty sure an algorithm exists which both complies with the law and decompiles fuzzy data to 'less fuzzy' data, i.e. identifying data, thus obviating the ban.
Yeah I don't understand how something like this makes any sense. Just average out the data points and you're going to get pretty much a straight line path of where and what somebody did. Maybe if there's like one data point per week per person, but even that's going to be pretty informative when averaged as to exactly where somebody lives, works, and so on.
Depends on if the independent events are uncorrelated. For example, if they had to be quantized to a grid of length 1750ft and quantized sufficiently in time as well, it might be impossible to average out since the various data points provide no new information.
This is why I posted the k-anonymity paper. A dataset is k-anonymous if using the dataset is is not possible to distinguish a given person in the dataset from k other people. Then we set a value of k we feel comfortable with and from that we can back into the areas which intuitively you would think would need to vary in size based on time of day and how many people travel through each area in the dataset.
It’s a lot of work but this is what you need to do to guarantee a given (non-zero) level of privacy. It can be done if people are serious about it.
I assume this depends if 1750ft means the location you can derive from the data (together with other data) or the precision of the individual data point