In today’s digitized world, data privacy is an important concern for both private and public organizations. This has led to an interesting challenge in the analytics industry, where data accessibility is key to the development of high-quality machine learning models.

When working on artificial intelligence projects in the real world, you will find that most datasets are siloed within large enterprises for two reasons:

First, organizations have legal requirements, which preclude them from sharing their datasets outside of their organization, to keep it safe from both accidental and intentional leakage.

Second, retaining large datasets collected from or about their customers also confers a competitive advantage.

This data can help organizations improve and personalize their products and services. And it makes a lot of sense if you think about it. It’s better to measure what your users like than to guess and build products that no one wants to use. But this can also be dangerous. It undermines the privacy of customers because the collected data can be sensitive, causing harm if leaked.

#2020 may opinions #ai #differential privacy #privacy