Lessons from improperly anonymized taxi logs

Posted to Statistics  |  Tags: ,  |  Nathan Yau

Through a Freedom of Information request Chris Whong received and eventually released NYC taxi logs starting in 2013 (about 173 million trips). Vijay Pandurangan looked at the data a little closer and deanonymized the logs to link hashed license numbers to the driver names. It didn’t take much to do it. Pandurangan described the process and lessons organizations can learn when they release data.

Someone on Reddit pointed out that one specific driver seemed to be doing an incredible amount of business. When faced with anomalous data like that, it’s good practice to weed out data error before jumping to conclusions about cheating taxi drivers. Also, I couldn’t shake the feeling that there was something about that encoded id number: “CFCD208495D565EF66E7DFF9F98764DA.” After a little bit of poking around, I realised that that code is actually the MD5 hash of the character ‘0’. This proved my suspicion that this was actually a data collection error, but also made me immediately realise that the entire anonymization process was flawed and could easily be reversed.

He also provided the code snippet he used to do it.


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