Adaptively Secure Streaming Functional Encryption

This chapter delves into the realm of adaptively secure streaming functional encryption (sFE), a cryptographic framework designed to handle dynamic and large-scale computational scenarios. The text begins by introducing functional encryption (FE) and its evolution, highlighting the need for fine-grained control over encrypted data. It then explores the limitations of traditional FE schemes, particularly their inability to handle incremental or streaming data and reveal interim results. The chapter introduces sFE as a solution to these limitations, detailing its framework and the first instantiation using standard FE. A significant portion of the text is dedicated to the challenges of achieving adaptive security in sFE, including the breakdown of classical techniques and the necessity of adaptive security for real-world applications. The authors present their innovative approach to constructing an adaptively secure sFE scheme, combining message-selective and function-selective schemes. The chapter also discusses the use of indistinguishability obfuscation and injective pseudorandom generators in achieving this security. Furthermore, it explores the applications of sFE in machine learning, particularly in training models on sensitive data while preserving privacy. The text concludes with a discussion on the future directions and open problems in the field of sFE, emphasizing the need for further research to address dynamic and large-scale computational scenarios.