TALK: Attribute-Based Encryption for random access machines
Dr. XIong Fan from UMCP on the latest research on ABE
Attribute based encryption (ABE) is an advanced encryption system with a built-in mechanism to generate keys associated with functions which in turn provide restricted access to encrypted data. Most of the known candidates of attribute based encryption model the functions as circuits. This results in significant efficiency bottlenecks, especially in the setting where the function associated with the ABE key is represented by a random access machine (RAM) and a database, with the runtime of the RAM program being sublinear in the database size. In this work we study the notion of attribute based encryption for random access machines (RAMs), introduced in the work of Goldwasser, Kalai, Popa, Vaikuntanathan and Zeldovich (Crypto 2013). We present a construction of attribute based encryption for RAMs satisfying sublinear decryption complexity assuming learning with errors; this is the first construction based on standard assumptions. Previously, Goldwasser et al. achieved this result based on non-falsifiable knowledge assumptions. We also consider a dual notion of ABE for RAMs, where the database is in the ciphertext and we show how to achieve this dual notion, albeit with large attribute keys, also based on learning with errors.
Joint work with Prabhanjan Ananth (UC Santa Barbara) and Elaine Shi (Cornell).
XIong Fan is a postdoc researcher at the University of Maryland, hosted by Prof. Jonathan Katz. He earned his PhD from Cornell University under the supervision of Prof. Elaine Shi. His main research interest is cryptography, security and programming languages. During his PhD, he spent the summer of 2017 interning in the Cryptography Research Group at IBM T. J. Watson Research Center, the summer of 2016 working with Dr. Vladimir Kolesnikov at Bell Labs, and the summer of 2015 with Dr. Juan Garay and Dr. Payman Mohassel at Yahoo Labs.
hosted by: Dr. Haibin Zhang