My Digital FootPrint

The question, “Is peanut butter good or bad for me?” frames many of the issues with questions that we pose. Peanut butter is neither good nor bad per say but a binary question ignores the complexity of the response that is needed. Is sharing data good or bad misses the complexity of what we believe, however qualifying why we do and don’t share data enables us to explore a more nuanced response. Fundamentally any question about sharing data should embrace two different starting positions and understand their impact on outcomes irrespective of agreements; we either believe that sharing data (in this case right now) increases value or decreases value. Why we share data Why we don’t share data I believe in transparency Transparency is a lie It will improve communication Communication will not improve I trust you I don’t trust you As a sign of commitment Sharing is not commitment Value for me will be created You will get the value I have nothing t...

{portability, sharing, mobility} this is used to provide the widest possible coverage of models and thinking. Portability that the data can move, sharing data as the ability to have access but does not need to move, and mobility as in the data is decentralized ------- Any discussion about personal data leads to opinions being shared about what it means (to someone) based on the position you start from; a personal view is different from a groups view, which is different from a citizens view and different again from an enterprise views. Inevitably there is a heated exchange as one of the parties believes in the purity of their view(point) and model to create a utopia and panacea for everyone. Once we grasp that there are massive gaps, voids and value in any of the starting positions; adding the complexity of assumptions, experiences and data itself, we can look at the different approaches and debate the wide range of solutions. The purpose of this post is to provide a framework...

The start of this was a directed ideation from  Ian Oppermann  NSW Chief Data Scientist and CEO of NSW Data Analytics Centre. The Challenge: How do you design a privacy preserving data sharing framework based on these papers. They are well written and provide a very good framework for the question. Privacy in Data Sharing — A Guide for Business and Government (Nov 2018) Data Sharing Frameworks — Technical White paper (Sept 2017) A write up from the original work is here.  https://medium.com/@tonyfish/black-swans-and-the-value-of-sharing-data-portability-mobility-900cf12d0c7c Focus Safe People, Safe Projects Within this context, what is a safe project and safe people for a privacy perserviing data sharing idea? Where “safe” means in this context — privacy preserving. Ignoring other factors such as sensitivity, importance, ethics and outcomes. Assumption 1. The reconstruction/ re-identification problem PII (personally identifiable information) ...

The rational your data should not be in a bank or a vault lies in history. When we centralise value (money for instance), it becomes attractive to rob, pinch, steal or walk off with - and protecting it comes at an every increasing cost. In the old language the rational could be summed up as "dynamite and vault"; in our modern language " very attractive  hacker economics"  The centralied deposit(s) become of great interest to those who want take it, control it, and to some who see it as a way to increase their own value.  Centralised works first for the institutions and second for the consumer.  A more subtle part of the conversation (should we trust banks with our data in a big vault) turns to where modern day value comes from with data and that is in the sharing of data. Data in a vault with no access (other than you with your key) has limited value. There is value if you want the bank to monitise your data on your behalf; but that is a different story/ pos...

I saw 50 black swans when thinking how data data portability/ mobility will migrate value towards the individual. Now some headlines are set up for click bait; however here is my picture taken in on the North Island New Zealand on lake Rotomahana when out walking and preparing this. Yes each little do it a black swan. Summary : Been thinking about the complex and hidden implications of the personal data portability/ mobility models and data sharing economics. The thinking leads to the possibility of making it far harder for large silo data owners to sell/ share their data due to risk of re-identification; which changes the data economy. Less general silo data being available for sale but increasing demand for ‘quality’ data could mean individual collated data becomes far more valued far quicker than forecast, as the value chain shifts in response to new legislation/ regulation. Early and fast adopting countries will benefit with significant increases in innovation, investment and ...