Why the search for a privacy-preserving data sharing mechanism is failing
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.
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.”
More recently, synthetic data has emerged as a new anonymization technique however the paper suggests that, in contrast to the promises made by its proponents, it is subject to the same privacy/utility trade-offs as the traditional anonymization of data.
Source: Why the search for a privacy-preserving data sharing mechanism is failing