Optimal Threshold Traitor Tracing

Threshold encryption distributes decryption capability across $n$ parties such that any $t$ of them can jointly decrypt a ciphertext, while smaller coalitions learn nothing. However, once $t$ or more parties collude, traditional threshold schemes provide no accountability: a coalition of $t$ or more parties can pool its keys into a pirate decoder that enables unrestricted decryption, all without any risk of being exposed. To address this, Boneh, Partap, and Rotem [CRYPTO '24] introduced threshold traitor tracing (TTT), which equips threshold encryption with traceability. Yet, all known TTT schemes either suffer from parameter sizes growing with at least $n^{1/3}$, or rely on indistinguishability obfuscation to achieve optimal parameters.

In this paper, we present the first TTT schemes with optimal parameters, where public keys, secret keys, and ciphertexts are all bounded by ${\sf poly}(\lambda,\log n)$, built solely from standard cryptographic tools and assumptions. Our first construction relies on the decisional Bilinear Diffie–Hellman (DBDH) assumption in prime order bilinear groups. Our second scheme is a candidate construction based on the Learning with Errors (LWE) assumption, which relies on the existence of secret sharing schemes with certain properties. This construction is plausibly post-quantum secure, and supports ramp-thresholds where decryption requires a larger coalition than those tolerated by security. Both of our constructions provide traceability against coalitions of arbitrary sizes.

To achieve these results, we introduce a new primitive, Attribute-Based Threshold Encryption (ABTE), which generalizes both threshold and attribute-based encryption. We then combine ABTE with Mixed Functional Encryption through a new compiler to obtain our TTT schemes. We believe ABTE is a powerful primitive that may have independent applications beyond optimal TTT.