cyberspace law committee meeting, august 3, 2012 big data lois mermelstein the law office of lois d....

Cyberspace Law Committee Meeting, August 3, 2012

Big DataLois MermelsteinThe Law Office of Lois D. Mermelsteinlois@loismermelstein.com512-222-8589

Ted ClaypooleWomble Carlyletclaypoole@wcsr.com704-331-4910

What Is Big Data?

✤ Data that exceeds the processing capacity of conventional database systems.

✤ Too much data

✤ It moves too fast

✤ It’s too diverse

How’d we get here?

✤ Storage, processing speed, and bandwidth are becoming exponentially faster

✤ Networking is expanding exponentially

✤ And you can buy all the pieces - data, infrastructure, processing

source: http://radar.oreilly.com/2011/08/building-data-startups.html

Crunching Big Data - Volume

✤ Turn 12 terabytes of tweets/day into improved product sentiment analysis

✤ Convert 350 billion annual meter readings to better predict power consumption

✤ Crunching Facebook recommendations based on your friends’ interests

Crunching Big Data - Velocity

✤ Time-sensitive analysis and decision-making - to catch important events as they happen

✤ When there’s too much input data (so toss some) or immediate decisions must be made

✤ Examples:

✤ Scrutinize 5 million trade events/day to identify potential fraud

✤ Analyze 500 million daily call detail records in real-time to predict customer churn faster

Crunching Big Data - Variety

✤ Not just names/addresses in a customer database

✤ Want to analyze text, sensor data, audio, video, location data, click streams, log files, and anything else that’s available

✤ Principle: when you can, keep everything - there might be something useful in what you throw away

Unexpected Consequences

✤ Anonymous AOL searcher isn’t (NYT, 8/9/2006)

✤ Anonymous Netflix users aren’t, when compared with IMDb database (Wired, 12/13/2007)

✤ For many, browsing history is unique and repeatable (8/1/2012)

✤ Target knows when you’re pregnant (NYT, 2/19/2012)

Lessons to (Re)learn

✤ Correlation isn't causation

✤ But correlation may be all you need

✤ You can't hide in the crowd

Personally Identifiable Information

PII as a mathematical function

How many points of data do you need?

Pineda v Williams Sonoma Stores, Inc. (Cal, Feb 10 2011)

HIPAA De-Identified Data

Re-Identifying De-Identified Data

Escaping Regulatory Requirements

Privacy

Fair Credit Reporting

Redlining

Employment Discrimination

Single Transaction Owned By:

Retailer

Wholesale vendor

Manufacturer

Shipping Company

Customer’s Bank

Customer’s ISP

Retailer’s Bank

Merchant Card Processor

Phone company/Hardware/Software

Government Using Big Data

Law Enforcement

Copyright Issues

Who owns the data?

Who owns the derivative works?

Combined data?