By Nick Halstead

DataScan: Issue #70





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June 24 · Issue #70 · View online

Curated digest on the world of data.

Verizon stops sharing customer location data with data brokers. A case against GDPR. Getting to trusted data. Blockchain isn’t a revolution.
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Verizon, Sprint, AT&T and T-Mobile stop sharing customer sharing location with data brokers. Devin Coldewey summarises the scandal:
Verizon sold bulk access to its customers’ locations to the brokers in question, LocationSmart and Zumigo, which then turned around and resold that data to dozens of other companies. This isn’t necessarily bad — there are tons of times when location is necessary to provide a service the customer asks for, and supposedly that customer would have to okay the sharing of that data. That doesn’t seem to have been the case at LocationSmart customer Securus, which was selling its data directly to law enforcement so they could find mobile customers quickly and without all that fuss about paperwork and warrants. And then it was found that LocationSmart had exposed an API that allowed anyone to request mobile locations freely and anonymously, and without collecting consent.
A case against the General Data Protection Regulation. Niam Yaraghi, nonresident fellow in the Brookings Institution’s Center for Technology Innovation, proposes an alternative perspective on the GDPR:
While GDPR is widely perceived as a major step forward, it is not yet clear how much of a meaningful impact it would have on consumers’ privacy and whether it will ultimately lower the cost and raise the quality of the services they receive. GDPR could increase the cost of the services that consumers are so used to receiving free of charge. In the pre-internet era, services cost actual money. With digitisation, consumers are now able to pay for the services they receive with their private information rather than their money.
Yaraghi also points out that without sharing information, “we would never have social networks” and search engines “would return irrelevant results”. He concludes that although GDPR creates “an illusion of privacy for a few”, it is “at the expense of the many”. ⚖️
Earlier this week, BT was fined £77,000 by the ICO for sending 5m spam emails between December 2015 and November 2016, and GCHQ began investigating the Dixons Carphone data breach, which involved the unauthorised access to 5.9m Dixons Carphone customers’ cards. 😬
Getting to trusted data via AI, machine learning and blockchain. Randy Bean, CEO of NewVantage Partners, discusses the implications and practicalities of MIT’s “Towards an Internet of Trusted Data: A New Framework for Identity and Data Sharing” paper. 
Bean notes the call for improving "the process and quality of data sharing”, which could be resolved by “moving the algorithm to the data” - so raw data never leaves its repository. 💯
Furthermore, Bean quotes David Shrier, lecturer and futurist with MIT Media Lab, commenting on the problems associated with current data storage architectures:
Blockchain is a completely different kind of database, one with the potential for greater transparency into the data for multi-stakeholder environments, and greater cyber-resilience if certain types of Blockchain and other technology are combined.The old-school concepts of data lake, data warehouse, and data mart still rely on the concept of having a centralised database which provides for a single point of failure and an attractive attack surface for hackers.
– Should linked datasets form part of your data strategy? 🤔
Blockchain isn’t a revolution. Kevin Werbach, Wharton professor and tech policy maven, calls for blockchain to stop being thought about as a “unitary phenomenon”, and instead be split up - “to assess developments accurately”. Werbach divides it into three areas: cryptocurrency for “trust-minimising”, blockchain for “tracking, and cryptoassets for "trading”:
The first truly is a revolutionary concept… but the jury is still out on whether the revolution will succeed. The second and third are game-changing innovations on the path to significant adoption… which are nonetheless essentially evolutionary.
👏  Anaconda reveals results of first ‘State of Data Science’ survey –> data scientists are trading traditional big data for cloud-native tech.
💯  The growing importance of data integration between departments.
🤖  Artificial intelligence trained to analyse causation.
🔍  The man who saw the dangers of Cambridge Analytica years ago. 
🌊  Is it really a good idea to dump data centres in the sea?
💼  What does GDPR mean for M&A due diligence?
💸  Backdoored images downloaded 5m times removed from Docker Hub.
💥  50 free datasets for machine learning. 
🚲  Copenhagenize your city: the case for urban cycling in 12 graphs:
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