"mining" entries

The technical aspects of privacy

The first of three public workshops kicked off a conversation with the federal government on data privacy in the US.

Thrust into controversy by Edward Snowden’s first revelations last year, President Obama belatedly welcomed a “conversation” about privacy. As cynical as you may feel about US spying, that conversation with the federal government has now begun. In particular, the first of three public workshops took place Monday at MIT.

Given the locale, a focus on the technical aspects of privacy was appropriate for this discussion. Speakers cheered about the value of data (invoking the “big data” buzzword often), delineated the trade-offs between accumulating useful data and preserving privacy, and introduced technologies that could analyze encrypted data without revealing facts about individuals. Two more workshops will be held in other cities, one focusing on ethics and the other on law. Read more…

How did we end up with a centralized Internet for the NSA to mine?

The Internet is naturally decentralized, but it's distorted by business considerations.

I’m sure it was a Wired editor, and not the author Steven Levy, who assigned the title “How the NSA Almost Killed the Internet” to yesterday’s fine article about the pressures on large social networking sites. Whoever chose the title, it’s justifiably grandiose because to many people, yes, companies such as Facebook and Google constitute what they know as the Internet. (The article also discusses threats to divide the Internet infrastructure into national segments, which I’ll touch on later.)

So my question today is: How did we get such industry concentration? Why is a network famously based on distributed processing, routing, and peer connections characterized now by a few choke points that the NSA can skim at its leisure?
Read more…

Four short links: 17 June 2009

Four short links: 17 June 2009

Word Mining, Open Ideas, Power Meter BotNet, and Realtime Web Traffic Tracking

  1. NY Times Mines Its Data To Identify Words That Readers Find Abstruse — the feature that lets you highlight a word on a NY Times web page and get more information about it is something that irritates me. I’m fascinated by the analysis of their data: boggling that sumptuary is less perplexing than solipsistic. Louche (#3 on the list) has been my favourite word for two years, by the way, since I heard Dylan Moran toss it out in that uniquely facile way the Irish have with words. I think Irish citizens get this incredible competence with the English language for free, along with staggering house prices and beer you can walk on.
  2. Open Ideas — Alex Payne’s blog of Concepts in the public domain, awaiting collaboration and appropriation.
  3. Buggy ‘smart meters’ open door to power-grid botnet (The Register) — Paul Graham said that we’ve found what we get when we cross a television with a computer: a computer. Similarly, intelligent power meters are computers, computers that apparently haven’t been well-secured. To prove his point, Davis and his IOActive colleagues designed a worm that self-propagates across a large number of one manufacturer’s smart meter. Once infected, the device is under the control of the malware developers in much the way infected PCs are under the spell of bot herders. Attackers can then send instructions that cause its software to turn power on or off and reveal power usage or sensitive system configuration settings.
  4. Chartbeat — the sexiest web analytics ever. It gives realtime count of users, whether they’re reading or writing (based on whether focus is in a form element), where they’re from, mentions on Twitter, and more and more and more. This is a different form of analytics than Google Analytics, which tells you trends and historical access. Love this for the pure sex appeal of a heads-up dashboard that can tell you exactly how many people are on your site and exactly what they’re doing. (via Artur)

Credit card company data mining makes us all instances of a type

The New York Times has recently published one of their in-depth,
riveting descriptions of how

credit card companies use everything they can learn about us
Any detail can be meaningful: what time of day you buy things, or the
quality of the objects you choose. So who gave them permission to use our purchase information against us? What law could possibly address this kind of power play? There’s another disturbing aspect to the data mining: it treats us all as examples of a pattern rather than as individuals.