May 19, 2018
“There's so much more information there than we know. Take a moment to think about what permissions you're giving."
Shafi Goldwasser is an award-winning mathematician and computer scientist and the Director of the Simons Institute for the Theory of Computing at UC Berkeley. Her most notable work is in cryptography and zero knowledge proof. We discuss the promise of cryptography to make our society more secure.
Data privacy and you
Cryptography is the field that deals with the privacy and correctness of how our information is used. It makes our data more secure, with a range of tools such as encryption, authentication, and verification. Every time we are online, we need to be vigilant about what private information we share and with whom. We should use the tools of cryptography and be careful about giving permissions for apps to access our data.
Algorithmic Fairness and Data Bias
We have an idea that algorithms are fair because they are machine computations. However, algorithms do no account for actual individuals, so the data is trained with existing societal norms, which can perpetuate unfairness. Data can also be poisoned once people figure out what algorithms are used by tweaking the information in order to get the desired outcome.
We must demand that our personal information is only used in ways that can keep our identity private. There are already collaborative platforms using various encryption methods that are effective for governments and companies to use. “If companies get into trouble because of fiascos having to do with private data you don’t just blindly continue supporting them.”
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Shafi Goldwasser is the Director of the Simons Institute for the Theory of Computing at UC Berkeley, the world’s leading venue for collaborative research in theoretical computer science. She is also the Professor of Electrical Engineering and Computer Science at MIT, and professor of computer science and applied mathematics at the Weizmann Institute of Science in Israel. She is currently working on the project “Splinter: Practical Private Queries on Public Data“.