Why the Search for a Privacy-Preserving Data Sharing Mechanism is Failing

upstart writes:

Why the search for a privacy-preserving data sharing mechanism is failing:

From banking to communication our modern, daily lives are driven by data with ongoing concerns over privacy. Now, a new EPFL paper published in Nature Computational Science argues that many promises made around privacy-preserving mechanisms will never be fulfilled and that we need to accept these inherent limits and not chase the impossible.

Data-driven innovation in the form of personalized medicine, better public services or, for example, greener and more efficient industrial production promises to bring enormous benefits for people and our planet and widespread access to data is considered essential to drive this future. Yet, aggressive data collection and analysis practices raise the alarm over societal values and fundamental rights.

As a result, how to widen access to data while safeguarding the confidentiality of sensitive, personal information has become one of the most prevalent challenges in unleashing the potential of data-driven technologies and a new paper from EPFL's Security and Privacy Engineering Lab (SPRING) in the School of Comupter and Communication Sciences argues that the promise that any data use is solvable under both good utility and privacy is akin to chasing rainbows.

Head of the SPRING Lab and co-author of the paper, Assistant Professor Carmela Troncoso, says that there are two traditional approaches to preserving privacy, "There is the path of using privacy preserving cryptography, processing the data in a decrypted domain and getting a result. But the limitation is the need to design very targeted algorithms and not just undertake generic computations."

The problem with this type of privacy-preserving technology, the paper argues, is that they don't solve one of the key problems most relevant to practitioners: how to share high-quality individual-level data in a manner that preserves privacy but allows analysts to extract a dataset's full value in a highly flexible manner.

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