Researchers have discovered that people tend to choose only a few hundred 4-digit combinations from 10,000 possible PINs. Knowing what those popular combinations are aids hackers greatly in tapping their victims' bank accounts.
Last week, a panel of three judges on the U.S. Court of Appeals for the 2nd Circuit heard arguments on the ACLU vs Clapper lawsuit against the U.S. government's domestic mass surveillance activities. The ACLU argues the surveillance violates the 4th Amendment while the federal government argued that the Patriot Act renders such activities lawful.
Members of the FierceBigData community with substantial experience in addressing privacy issues responded with some outstanding insights and some actionable best practices for individual researchers.
While many researchers and privacy advocates hail the de-identification route to protect privacy, others say that simply won't do because anonymization makes a mess of the data sets. To make the data more open and useful, one group of researchers recommends we stop trying to de-identify private data and hold researchers responsible for protecting privacy instead.
Using analytics only for internal reflection is myopic and dangerous. Why? Because you may very well find yourself improving processes and products that will soon become obsolete and understanding customers who are on their way out the door…forever.
Protecting company data has always been a difficult challenge, and with every passing day it becomes harder to do. As Mary-Pat Cormier says in her guest post today, "companies have become aware that the risk of being hacked is unavoidable." Fortunately, she has some very good advice to share on what to do to financially and legally protect your company before and after a breach.
Companies are typically buying big data tools before they know what they want to do with them. That's like buying the best fishing pole on the market only to discover that now you have to build a house with it.
Data Divination: Big Data Strategies was written to focus entirely on strategy from how to accurately calculate ROI, present a winning business case and empower your workforce from the CEO down, to developing overall and project-specific strategies that actually work.
If statisticians learn to do more than write code in the way of computing skills and data scientists begin to perfect their skills in statistical analysis--might the two professions merge into one? And if so, what title will be etched upon their office door?
Got something to say or ask about big data? Email it to me with permission to publish and you just might find yourself in the Spotlight section in an issue soon!