Why a DNA data breach is much worse than a credit card leak

You can’t change your DNA
By Angela Chen@chengela Jun 6, 2018, 3:54pm EDT

This week, DNA testing service MyHeritage revealed that hackers had breached 92 million of its accounts. Though the hackers only accessed encrypted emails and passwords — so they never reached the actual genetic data — there’s no question that this type of hack will happen more frequently as consumer genetic testing becomes more and more popular. So why would hackers want DNA information specifically? And what are the implications of a big DNA breach?

One simple reason is that hackers might want to sell DNA data back for ransom, says Giovanni Vigna, a professor of computer science at UC Santa Barbara and co-founder of cybersecurity company Lastline. Hackers could threaten to revoke access or post the sensitive information online if not given money; one Indiana hospital paid $55,000 to hackers for this very reason. But there are reasons genetic data specifically could be lucrative. “This data could be sold on the down-low or monetized to insurance companies,” Vigna adds. “You can imagine the consequences: One day, I might apply for a long-term loan and get rejected because deep in the corporate system, there is data that I am very likely to get Alzheimer’s and die before I would repay the loan.”

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The (Data Science) Notebook: A Love Story by David Wallace

Computational notebooks for data science have exploded in popularity in recent years, and there’s a growing consensus that the notebook interface is the best environment to communicate the data science process and share its conclusions. We’ve seen this growth firsthand; notebook support in Mode quickly became one of our most adopted features since launched in 2016.

This growth trend is corroborated by Github, the world’s most popular code repository. The amount of Jupyter (then called iPython) notebooks hosted on Github has climbed from 200,000 in 2015, to almost two million today. Data from the nbestimate repository shows that the number of Jupyter notebooks hosted on GitHub is growing exponentially:

This trend begs a question: What’s driving the rapid adoption of the notebook interface as the preferred environment for data science work?

Inspired by an Analog Ancestor

The notebook interface draws inspiration (unsurprisingly) from the research lab notebook. In academic research, the methodology, results, and insights from experiments are sequentially documented in a physical lab notebook. This style of documentation is a natural fit for academic research because experiments must be intelligible, repeatable, and searchable.

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