Carnegie Mellon University researcher, Tom Mitchell|, says that privacy risks "on a scale that humans have never before faced" hinder real-time data analysis that could be used to solve health, traffic and human behavior problems.
Mitchell outlines his ideas in a column in Friday's edition of the journal Science.
Of course privacy in an increasingly online world continues to grab headlines, the latest regarding Facebook's controversial privacy setting changes. Mobile marketers have also received the attention of privacy defenders.
Mitchell, head of the Machine Learning Department in CMU's School of Computer Science, says privacy will be a growing concern as data mining techniques once used largely for relatively behind-the-scenes scientific and financial analysis expand to usage related to more personal activities. Such expansion could include monitoring smartphones for the purpose of reducing traffic congestion or even giving people a head's up if they've been near someone with a contagious disease, Mitchell says (some of these uses are already happening in a limited way).
While privacy concerns are considerable, Mitchell says that technical solutions can be developed to address such concerns. For example, one way to protect data privacy is to mine data across organizations without aggregating it in one repository (separate organizations would analyze data, then encrypt the results before pooling it with others' results).
"Perhaps even more important than technical approaches will be a public discussion about how to rewrite the rules of data collection, ownership, and privacy to deal with this sea change in how much of our lives can be observed, and by whom," Mitchell writes, according to CMU. "Until these issues are resolved, they are likely to be the limiting factor in realizing the potential of these new data to advance our scientific understanding of society and human behavior, and to improve our daily lives."