[DATA PRIVACY] - Preserving Privacy While Sharing Data

Differential privacy can safeguard personal information when data is being shared, but it requires a high level of expertise.

- SIMSON L. GARFINKEL AND CLAIRE MCKAY BOWEN

As organizations increasingly seek to exploit data, both for internal use and for sharing with partners in digital ecosystems, they face more laws mandating stronger consumer privacy protections. Unfortunately, traditional approaches to safeguarding confidential information can fail spectacularly, exposing organizations to litigation, regulatory penalties, and reputational risk.

Since the 1920s, statisticians have developed a variety of methods to protect the identities and sensitive details of individuals whose information is collected. But recent experience has shown that even when names, Social Security numbers, and other identifiers are removed, a skilled hacker can take the redacted records, combine them with publicly available information, and reidentify individual records or reveal sensitive information, such as the travel patterns of celebrities or government officials.

The problem, computer scientists have discovered, is that the more information an organization releases, the more likely it is that personally identifiable information can be uncovered, no matter how well those details are protected. It turns out that protecting privacy and publishing accurate and useful data are inherently in opposition.

In an effort to tackle this dilemma, computer scientists have developed a mathematical approach called differential