Scalable Private Signaling
Sashidhar Jakkamsetti, Zeyu Liu, Varun Madathil · 2025
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, a private message is typically posted on the board for a recipient and the privacy requirement is that no one can determine the sender and 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 securely 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 work while providing full privacy. Our construction makes use of a trusted execution environment (TEE) and an Oblivious RAM (ORAM) 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. Furthermore, we implement a side-channel resistant prototype and show that for a server with a million recipients and ten million messages, the prototype takes less than 70 milliseconds to process a sent message and less than 6 seconds to process a retrieval request (for 100 signals) from a recipient.