Big data may not know your name, but it knows everything else
Two things I'd like to say here<p>1. All anonymisation algorithms (k-anonymity, l-divergence, t-closeness, e-differential privacy, (e,d)-differential privacy, etc.) have, as you can see, at least one parameter that states <i>to what degree</i> the data has been anonymised. This parameter should not be kept secret, as it tells entities that are part of a dataset how well, and in what way, their privacy is being preserved. Take something like k-anonymity: the k tells you that every equivalence class in the dataset has a size >= k, i.e. for every entity in the dataset, there are at least k-1 other identical entities in the dataset. There are a lot of things wrong with k-anonymity, but at least it's transparent. Tech companies however just state in their Privacy Policies that "[they] care a lot about your privacy and will therefore anonymise your data", without specifying <i>how</i> they do that.<p>2. Sharing anonymised data with other organisations (this is called Privacy Preserving Data Publishing, or PPDP) is virtually always a bad idea if you care about privacy, because there is something called the privacy-utility tradeoff: you either have data with sufficient utility, or you have data with sufficient privacy preservation, but you can't have both. You either publish/share useless data, or you publish/share data that does not preserve privacy well. You can decide for yourself whether companies care more about privacy or utility.<p>Luckily, there's an alternative to PPDP: Privacy Preserving Data Mining (PPDM). With PPDM, data analysts can submit statistical queries (queries that only return aggregate information) to the owner of the original, non-anonymised dataset. The owner will run the queries, and return the result to the data analyst. Obviously, one can still infer the full dataset as long as they submit a sufficient number of specific queries (this is called a Reconstruction Attack). That's why a privacy mechanism is introduced, e.g. epsilon-differential privacy. With e-differential privacy, you essentially <i>guarantee</i> that no query result depends significantly on one specific entity. This makes reconstruction attacks impossible.<p>The problem with PPDM is that you can't sell your high-utility "anonymised" datasets, which sucks if you're a big boi data broker.