The uncontrolled growth in domains such as surveillance systems, health care services, and finance produce a large amount of data and contain potentially sensitive data that can become public if they are not appropriately sanitized.
Motivated by this issue, we introduce a privacy filter (PF), a novel non-negative matrix factorization (NMF) framework aiming to preserve the privacy of data before publishing based on an alternating non-negative least squares (ANLS) approach.
More specifically, this framework enables data holders to choose the data dimension that protects user privacy without being aware of the privacy leakage.
We also consider the problem of privately learning a PF across multiple sensitive datasets, leading to a federated learning algorithm that guarantees private data protection and high accuracy classification for non-private information.
Finally, the experiments conduct and illustrate the superior performance of the proposed algorithms under the premise of protecting users’ private data.
Keywords: Data privacy, distributed data privacy, privacy-preserving machine learning, adversarial learning, no-negative matrix factorization