71–80 of 99 posts
Upload my data to be anonymized? Nooooooooope
For data to be anonymous under GDPR, it is not enough that individuals cannot be identified from the anonymized data set. If individuals can be identified when the anonymous data set is compared with the source data set, the anonymized data is not "anonymous". For data to be truly anonymous under GDPR. there must be no other additional data that would allow for reidentification. If there is any other data that, when…
Fair warning: anonymization is a hard problem. It is never easy, and you'd be surprised how many bits can leak out of what you thought was properly anonymized data. If you are using data for test purposes please use generated data, not anonymized data. This has the additional advantage that there is no potential path for live data to end up on a developers machine. added in edit: And also realize that just using a se…
Amnesia is an application written in java and JavaScript and should be used locally for anonymizing a dataset.
Upload my data to be anonymized? Nooooooooope
Amnesia is an application written in java and JavaScript and should be used locally for anonymizing a dataset.
Fair warning: anonymization is a hard problem. It is never easy, and you'd be surprised how many bits can leak out of what you thought was properly anonymized data. If you are using data for test purposes please use generated data, not anonymized data. This has the additional advantage that there is no potential path for live data to end up on a developers machine. added in edit: And also realize that just using a se…
Fair warning: anonymization is a hard problem. It is never easy, and you'd be surprised how many bits can leak out of what you thought was properly anonymized data. If you are using data for test purposes please use generated data, not anonymized data. This has the additional advantage that there is no potential path for live data to end up on a developers machine. added in edit: And also realize that just using a se…
What about building machine learning models that make predictions on said data? Can't just test on fake data.
The problem goes beyond testing: your training might (1) be deprived of information your original data had, e.g., among a 1000 features, the key to the classification of interest might be one feature; how does your generator know to not distort this potentially at the cost of distorting the other 999 features? (2) might latch onto the assumptions made by the data generator, e.g., for a continuous valued feature, your model might bias itself towards the distribution moments that the generator assumed.
I am not sure generating good fake data is a problem different from good density determination. And in some cases, you might need to specify what parts of the original distribution you don't want the generator to mess with: consider an NLP dataset where your model must rely on sentence structure. Generating the right bag-of-words features might not help here: sequence matters. Or if you wanted to use contextual embeddings; sequence matters then too.
Even if you did manage to generate a "distributionally-compatible" version of the data, for cases where you perform some kind of data enrichment at a later stage, you could run into problems. For ex: if the original data has zipcodes that you wanted to mask, and your data generator substitutes them with arbitrary strings, then at a later point you cannot introduce a feature that measures the proximity of two locations.
For data to be anonymous under GDPR, it is not enough that individuals cannot be identified from the anonymized data set. If individuals can be identified when the anonymous data set is compared with the source data set, the anonymized data is not "anonymous". For data to be truly anonymous under GDPR. there must be no other additional data that would allow for reidentification. If there is any other data that, when…
Can someone explain the point of this requirement? If a malicious actor has access to the source data there's no need to compare it to anonymized data. What am I missing?
The thing that you're worried about with poorly-anonymized datasets is that if you have another non-anonymized dataset you can combine them to deduce the original information. "Your data set must not be able to be combined with any others that would allow them to infer the original data" is hard. How could you possibly test them all?
Well it turns out that there is one such non-anonymized dataset with the property that if you can't connect your anonymized data with it at all then you can be pretty sure that you couldn't connect them with any others -- the original data!
Fair warning: anonymization is a hard problem. It is never easy, and you'd be surprised how many bits can leak out of what you thought was properly anonymized data. If you are using data for test purposes please use generated data, not anonymized data. This has the additional advantage that there is no potential path for live data to end up on a developers machine. added in edit: And also realize that just using a se…
When a massive object passes between a distant star and an Earth-based observer the light coming from the star gets deflected and focused by the gravity of the massive object. The star seemingly brightens as a result. This is called gravitational microlensing. The duration and how the brightening happens allowed scientists to determine that it was likely a Mars-sized planet and that it likely had no star within 8 astronomical units. It's likely a rogue planet of roughly Earth size.
Think about how little actual information these astronomers had. Yet they were able to make a very credible prediction on what happened. You see this all over in science, particularly in physics, where the truth is coaxed out of very little direct data. This makes me think that similar things can probably be done with data about people. This would mean that effectively anonymizing people's data is very hard or maybe even impossible.
Earlier quoted context omitted.
This isn't my area of expertise, but I've spoken to computer vision researchers who apparently use generated data for training models for self-driving vehicle autonomy. Maybe they only use generated data for the train set and then do cross-validation on real data? I'd like to hear them chime in on this thread if any are reading here. Theoretically speaking if the generated data has the same distribution and parameter…
You could kickstart training on simulators and then do a transfer, i.e. make adjustments to your final model, on real world data. But to learn only on generated data the problem boils down to the nonparametric features you will be using to state that the generated data is similar to the real data. What is a complex enough feature to say that images are equivalent? They might be statistically equivalent according to y…