# Does Hashing data affect linear separability

What would be a good way to obfuscate sensitive information and store e-commerce transaction data, to later perform fraud analysis on it. One idea that crossed my mind, was to hash each sensitive field with a hash function (e.g. murmur3 128 bit) and store. As an example if we had a column account holder name with a value John Doe, the hash operation produces some 128 bit value for John Doe. Due to the property of statistical randomness introduced by the hash algorithm, combined with the cascading effect, would this affect the property of Linear separability of the underlying data?

EDIT: Following up from the helpful comment by @AlexeyGrigorev. I do understand that just hashing the name would not contribute to obfuscation. The data I am preparing at the moment for my academic interests, has lot more sensitive information (fields like card info, etc ). I have all fields in the data hashed with the same hashing function. The hashed data is now has score of 0.71 and AUCROC of 0.75 with best tuned SVM (tuned RBF kernel). As my hashing function introduces a random distribution of the data in a $2^{128}$ space, so I am guessing it should have affected the linear separability of the underlying data. Correct me if I am wrong or wandering in offshoot irrational directions.

• I don't think the name would be a particularly useful feature anyway, so hashing it shouldn't make it worse. But speaking of obfuscation, just hashing the name may not be enough to truly anonymize the data set – Alexey Grigorev Jul 12 '15 at 10:58
• How is your RBF kernel dealing with 128 bit data? – Memming Jul 12 '15 at 13:10
• @Memming I actually followed the process described here and then I used Scaling to scale it down, before feeding into SVM. – Segmented Jul 14 '15 at 0:00