Machine learning (ML) models are a cornerstone of modern technology, allowing models to learn from and make predictions based on vast amounts of data. These models have become integral to various industries in an era of rapid technological innovation, driving unprecedented advancements in automation, decision-making, and predictive analysis. The reliance on large amounts of data, however, raises significant concerns about privacy and data security. While the benefits of ML are manifold, they are not without accompanying challenges, particularly in relation to privacy risks. The intersection of ML with privacy laws and ethical considerations forms a complex legal landscape ripe for exploration and scrutiny. This article will explore privacy risks associated with ML, privacy in the context of California’s privacy legislation, and countermeasures to these risks. Privacy Attacks on ML Models There are several distinct types of attacks on ML models, four of which target the privacy of protected information . Model Inversion Attacks constitute a sophisticated privacy intrusion where an attacker endeavors to reconstruct original input data by reverse-engineering a model’s output. A practical illustration might include an online service recommending films based on previous viewing habits. Through this method, an attacker could deduce an individual’s past movie choices, […]
