Privacy's Random Answer

"Privacy's Random Answer"
CNet (11/24/04); Kanellos, Michael

Michael Kanellos is impressed by IBM's work with data randomization as a possible solution to the increasingly contentious issue of consumer privacy, which is fueled by consumers' growing outrage with how companies and organizations collect, exchange, and distribute their personal information. The idea is that data randomization would use indecipherable mathematical calculations to effectively scramble consumer data such as age, income, past purchases, or medical information while allowing back-end systems to discern patterns within the customer base. The randomization system uses Bayesian probability to determine the relationship between different values, so that consumers do not have to falsify their data, which is randomized before being transmitted to the corporate server. The back-end computer attempts to ascertain the randomizing calculations employed to conceal the original values, so that accurate customer base simulations can be extrapolated. "I think the key insight was that you don't have to have access to precise information to build good models," explains IBM senior fellow Rakesh Agrawal, who is directing the data randomization project. In several trials, there was a mere 2 percent to 3 percent difference between the curve plotted by the original data and the reconstructed curve. Among the areas Agrawal believes could benefit from data randomization technology is hospital services, which could provide records about disease epidemics without fear of litigation. In addition, large businesses could share their data without revealing customer lists, while network security could be bolstered.
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