Obfuscation of sensitive data for machine learning - machine-learning

Question

I am preparing a dataset for my academic interests. The original dataset contains sensitive information from transactions, like Credit card no, Customer email, client ip, origin country, etc. I have to obfuscate this sensitive information, before they leave my origin data-source and store them for my analysis algorithms. Some of the fields in data can be categorical and would not be difficult to obfuscate. Problem lies with the non-categorical data fields, how best should I obfuscate them to leave underlying statistical characteristics of my data intact but make it impossible (at least mathematically hard) to revert back to original data.
EDIT: I am using Java as front-end to prepare the data. The prepared data would then be handled by Python for machine learning.
EDIT 2: To explain my scenario, as a followup from the comments. I have data fields like:
'CustomerEmail', 'OriginCountry', 'PaymentCurrency', 'CustomerContactEmail',
'CustomerIp', 'AccountHolderName', 'PaymentAmount', 'Network',
'AccountHolderName', 'CustomerAccountNumber', 'AccountExpiryMonth',
'AccountExpiryYear'
I have to obfuscate the data present in each of these fields (data samples). I plan to treat these fields as features (with the obfuscated data) and train my models against a binary class label (which I have for my training and test samples).

Answer 1

There is no general way to obfuscate non categorical data as any processing leads to the loss of information. The only thing you can do is try to list what type of information is the most important one and design transformation which leaves it. For example if your data is Lat/Lng geo position tags you could perform any kind of distance-preserving transformations, such as translation, rotations etc. if it is not good enough you can embeed your data in lower dimensional space while preserving the pairwise distances (there are many such methods). In general - each type of non-categorical data requires different processing, and each destroys information - it is up to you to come up with the list of important properties and finding transformations preserving it.

Answer 2

I agree with #lejlot that there is no silver bullet method to solve your problem. However, I believe this answer can get you started thinking about to handle at least the numerical fields in your data set.
For the numerical fields, you can make use of the Java Random class and map a given number to another obfuscated value. The trick here is to make sure that you map the same numbers to the same new obfuscated value. As an example, consider your credit card data, and let's assume that each card number is 16 digits. You can load your credit card data into a Map and iterate over it, creating a new proxy for each number:
Map<Integer, Integer> ccData = new HashMap<Integer, Integer>();
// load your credit data into the Map
// iterate over Map and generate random numbers for each CC number
for (Map.Entry<Integer, Integer> entry : ccData.entrySet()) {
Integer key = entry.getKey();
Random rand = new Random();
rand.setSeed(key);
int newNumber = rand.nextInt(10000000000000000); // generate up to max 16 digit number
ccData.put(key, newNumber);
}
After this, any time you need to use a credit card num you would access it via ccData.get(num) to use the obfuscated value.
You can follow a similar plan for the IP addresses.