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Seminar Series

September 15

Binary Error Correcting Codes with Minimal Noiseless Feedback

Rachel Zhang
CSAIL MIT

Part Of

10:30A
- 12:00P

Location

G-882 Hewlett Room
Add to Calendar 2023-09-15 10:30:00 2023-09-15 12:00:00 America/New_York Binary Error Correcting Codes with Minimal Noiseless Feedback In an error correcting code with feedback, Alice wishes to communicate a k-bit message to Bob by sending a sequence of bits over a channel while receiving noiseless feedback about what has been received. It has long been known (Berlekamp, 1964) that in this model, Bob can still correctly determine x even if 1/3 of Alice's bits are flipped adversarially. This improves upon the classical setting without feedback, where recovery is not possible for error fractions exceeding 1/4.The original feedback setting assumes that Alice receives feedback each time she transmits a bit. In this talk, we will discuss the limited feedback model, where Bob is only allowed to send a few bits of feedback at a small number of pre-designated points in the protocol. We will give optimal constructions of feedback codes for both the error and erasure settings and prove matching lower bounds.Joint work with Meghal Gupta and Venkatesan Guruswami. https://arxiv.org/pdf/2212.05673.pdf G-882 Hewlett Room

June 28

ConSeal: A Secure Analytics Platform

Roberta De Viti
MPI-SWS

Part Of

4:00P
- 5:00P

Location

32-G449 KIVA
Add to Calendar 2023-06-28 16:00:00 2023-06-28 17:00:00 America/New_York ConSeal: A Secure Analytics Platform Many types of analytics on personal data can be made differentially private, thus alleviating concerns about the privacy of individuals. However, no analytics platform currently exists that can technically prevent data leakage and misuse with minimal trust assumptions; as a result, analytics that would be in the public interest are not done in privacy-conscious societies. To bridge this gap, we present secure selective analytics (SSA), where data sources can a priori restrict the use of their data to a pre-defined set of privacy-preserving analytics queries performed by a specific group of analysts, and for a limited period. Furthermore, we show that a scalable SSA platform can be built in a strong threat model based on minimal trust.In this talk, I will present ConSeal, an SSA platform that relies on a minimal trust implementation of functional encryption (FE), using a combination of secret sharing, secure multi-party computation (MPC), and trusted execution environments (TEEs). ConSeal tolerates the compromise of a subset of TEE implementations as well as side channels. Despite the high cost of MPC, we show that ConSeal scales to very large databases using MapReduce-based query parallelization. 32-G449 KIVA