Security needed will be minimal.
It is recommended that a company just protects the original data with enough security such that only the privacy program can access it to produce privatized data. If necessary, a company can also install security measures to prevent theft of the privatized data, which exposes data trends. Even if the privatized data falls into the wrong hands, it is very unlikely that individual privacy will be exposed.
Our software is compatible with most security systems.
Databases that can format information in terms of rows and columns. So, most databases that store metadata.
Sure, we can integrate it into any setting you have in mind.
Has worked with Saibal Banerjee to develop the product. He has also done research in data privacy under a renowned professor (Prof. Clifton) in the field, so he is intimately familiar with the technical details of the product. He also works on the business strategy with Sunil and thus acts as a bridge between the technical side and the business side of Pridatex.
Has 30 years of machine learning, data, and algorithms development experience in Silicon Valley industries. Also has a Ph.D. in Computer Science with numerous publications and 15+ issued U.S. patents. He has also created his own Networking startup during the latter half of the 2000’s.
Is a renowned Purdue professor with 10 years of research and publication experience in Differential Privacy. He is contracted by the U.S. government to aid in the Census Bureau’s adoption of Differential Privacy. He is a valuable asset in guiding us to develop our solution with strong privacy protection guarantees.