Twelve Million Phones, One Dataset, Zero Privacy is part one of One nation, tracked, an New York Times investigation series.
Twelve Million Phones, One Dataset, Zero Privacy
is part one of One nation, tracked, an New York Times investigation series of smart phone information tracking and by Stuart A. Thompson and Charlie Warzel, within their privacy project. The research covers multiple topics, only starting out with an analysis of the potential contained in smartphone tracking information.
What we learned from the spy in your pocket.
Twelve Million Phones, One Dataset, Zero Privacy
The authors analyse a large dataset of location information from New York and Washington, DC, cell phone users. With the analysis, the article debunks myths about data privacy. The key takeaway of the analysis, to my interpretation are:
Data is not safe – the authors point out complex relationships of companies in the tracking business. Complexity makes it impossible to ensure ownership. There is no foolproof way for anyone or anywhere in the chain to prevent data from falling into the hands of a foreign security service.
Affected persons cannot consent – the authors criticism seems reasonable. Virtually all companies involved with tracking require user consent. And even cell phones make the geo-tracking feature visible to users. Only barely anyone in the business makes purpose transparent. In other words, no company prominently announce how they package and sell data or insight.
One Nation, Tracked
The article is a creepy read, but worth the time spending. The series One Nation, Tracked continues with 6 other parts:
Moore’s Law in Action: You’ll probably remember the prediction back from your days in University. In essence, Mr. Moore, founder of Fairchild Semi and CEO of Intel, predicted the density of transistors in modern integrated systems will double about every 18 months. He was right for a long time, while many predicted the end of his law. Visual Capitalist today linked a illustration showing the law in Action up to 2019.
Can the predictions from Moore’s Law keep up with technological innovation spanning almost 50 years? Watch this stunning animation to find out.
WTF of the day. The most advanced MySQL raytracer on the market right now. A raytracer, written in a single SELECT statment, that MySQL is able to process into an image. Pure Demoscene spirit here, whatever it is, make it run an animation or raytrace some spheres, something beautiful it was not meant to produce in first place.
In an ideal world, product managers have plenty of data they can use to validate their idea before building the wrong product. Yana Yushkina describes her journey from a Data Analyst to a Product Manager.
She talks about characteristics a good PM should bring, that include foundational analytical understanding, curiosity not just for technology but to search for the right answers in data, a sense of responsibility and the ability to communicate.
All of that combined with the right metrics at hand and self sufficient mindset will give a Product Manager the right answer from data.
In today’s edition of privacy related topics, it is Google that apparently stored customer passwords in plaintext. Google didn’t disclose which (enterprise) customers have been affected, but was clear that improper access is out of question. With this recent incident, Google joins ranks of Facebook, Instagram, but also Twitter and LinkedIn.
Google says it discovered a bug that caused some of its enterprise G Suite customers to have their passwords stored in an unhashed form for about 14 years.
It’s the Tech perspective, but has the potential for a good debate. Under GDPR it’s not even compliant and still plenty of companies collect all data they can get hold of. Driven by Big Data vendors telling the narrative of Data Lakes, that only require you the data today, should you want to ask any question you don’t know yet in the future.
Only – have you ever come up with a question that you could not answer based on the data that is already available? Based on data that you collected in a Data Lake?
Big disclaimer: personally I don’t conclude with the assumptions made in the initial article, but the question is worth thinking about. In particular because most organizations I met until today are not metric driven in first place.
Chris Stucchio hat einen interessanten Punkt bzgl. Big Data. Nämlich, dass Big Data heißt gar nicht Big ist. Nur weil Excel einen Datensatz nicht öffnen kann, heisst das nicht automatisch, dass Big Data Tools – in seinem Aufsatz Hadoop – notwendig sind. Gigabyteweise Daten lassen sich auch anders verarbeiten. Moderne Hardware hat nicht selten mehrere GB an Hauptspeicher. Die Geräte bei Mediamarkt waren vergangenen Samstag alleine mit mindestens 8Gb ausgestattet. Das heisst natürlich nicht, dass Hadoop oder vergleichbare Tools gar keinen Anwendungsfall hätten, aber die Use-Cases sind vielleicht anders gelagert, als man auf den ersten Gedanken annimmt.
Bruce Schneier wrote a book about Big Data, mass surveilance and the Internet of Things. Schneier talks about how this effects society and what to do about the increasing datarization of everything we’re doing.
The six issues that must be addressed are:
- Breach notification and data residency
- Data management at rest
- Data protection in motion
- Encryption key management
- Access controls
- Long-term resiliency of the encryption system