Accessing Data Privately, from Theory to Practice with Elaine Shi | a16z crypto research talks

Many high-value applications require privately accessing a large global database. For example, in private contact discovery, users want to fetch their friends' records without disclosing who their friends are. Large Language Models (LLMs) often need to retrieve relevant locations from a large knowledge base depending on the users' queries, and the access patterns must be protected if the queries are privacy-sensitive. In this talk, Elaine Shi (Carnegie Mellon University, Oblivious Labs) describes several techniques for achieving access pattern privacy, including the use of Oblivious RAM (ORAM) in conjunction with trusted hardware, Garbled RAM, as well as Private Information Retrieval (PIR). She compares these techniques, and does a reality check on their practicality today in real-life application scenarios. She describes two very simple constructions to illustrate how to construct practical ORAM and PIR, and also provides a historical view on how we got here, and look forward to the exciting research and opportunities going forward. About the presenter Elaine is a professor in Carnegie Mellon University, where she also obtained her Ph.D. She is a co-founder of Oblivious Labs. Her research interests include cryptography, mechanism design, foundations of blockchains, and programming languages. Prior to returning to CMU, she was on the tenure-track faculty of UMD and Cornell. Her work on ORAM and privacy-preserving algorithms have been implemented by Signal, Meta, Google, and other industry leaders. About a16z crypto research a16z crypto research is a multidisciplinary lab that works closely with our portfolio companies and others toward solving the important problems in the space, and toward advancing the science and technology of the next generation of the internet. More about us: a16z.com/2022/04/21/announcing-a16z-crypto-research More from the a16z crypto team Subscribe to our 'web3 weekly newsletter': a16zcrypto.substack.com Listen to our 'web3 with a16z' podcast: a16zcrypto.com/web3-with-a16z-podcast/ Read more of our work: a16zcrypto.com