Protecting Patron Privacy Pythonically
Do you handle patron data? Would you like to, but the fear of compromising your patrons’ privacy provokes paralysis? Do you handle it all the time and wonder what’s the BFD about privacy anyway, it’s just a book checkout, rite? Would you like to have confidence that you are managing your patron data responsibly, so that you can do something useful with it?
If you answered “yes” to any of these questions, this workshop is for you. In it, we will discuss and implement strategies for de-identifying and anonymizing personally identifiable information (PII) within patron data. We will do this by simulating the Transform step of an Extract, Transform, Load (ETL) workflow. We will use the python programming language, due to its easy setup, gentle learning curve, availability of useful libraries for this purpose, and popularity for ETL applications. However, the concepts and techniques covered could be ported to other environments and/or languages as needed. While experienced programmers are welcome, this workshop is intended to be useful to you even if you have minimal programming experience.
This will be a hands-on workshop, so you should bring a laptop upon which you have installed or can install the relevant software. Instructions will be provided in advance of the workshop and support will be available on the day in case you need it. In order to get the most out of the material, you should be comfortable with programmatic thinking. Familiarity with fundamental programming concepts (variable assignment, looping, etc) would also be helpful. If you’re not sure if this is the right workshop for you, we’d be happy to talk before you register.
