Big data may not know your name, but it knows everything else
Two things I'd like to say here1. 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 to what degree 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 how they do that.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.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 guarantee that no query result depends significantly on one specific entity. This makes reconstruction attacks impossible.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.