Welcome to the resource topic for 2023/572
Title:
Scalable Private Signaling
Authors: Sashidhar Jakkamsetti, Zeyu Liu, Varun Madathil
Abstract:Private messaging systems that use a bulletin board, like privacy-preserving blockchains, have been a popular topic during the last couple of years.
In these systems, typically a private message is posted on the board for a recipient and the privacy requirement is that no one can determine the sender and the recipient of the message.
Until recently, the efficiency of these recipients was not considered, and the party had to perform a naive scan of the board to retrieve their messages.
More recently, works like Fuzzy Message Detection (FMD),
Private Signaling (PS),
and Oblivious Message Retrieval (OMR)
have studied the problem of protecting recipient privacy
by outsourcing the message retrieval process to an untrusted server.
However, FMD only provides limited privacy guarantees, and PS and OMR greatly lack scalability.
In this work, we present a new construction for private signaling which is both asymptotically superior and concretely orders of magnitude faster than all prior works while providing full privacy.
Our constructions make use of a trusted execution environment (TEE) and an Oblivious RAM to improve the computation complexity of the server.
We also improve the privacy guarantees by keeping the recipient hidden even during the retrieval of signals from the server.
Our proof-of-concept open-source implementation shows that for a server serving a hundred thousand recipients and ten million messages, it only takes < 6 milliseconds to process a sent message, and < 200 milliseconds to process a retrieval (of 100 signals) request from a recipient.
ePrint: https://eprint.iacr.org/2023/572
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