PASTA: PASsword-based Threshold Authentication [article]

Shashank Agrawal, Peihan Miao, Payman Mohassel, Pratyay Mukherjee
2018 IACR Cryptology ePrint Archive  
Token-based authentication is commonly used to enable a single-sign-on experience on the web, in mobile applications and on enterprise networks using a wide range of open standards and network authentication protocols: clients sign on to an identity provider using their username/password to obtain a cryptographic token generated with a master secret key, and store the token for future accesses to various services and applications. The authentication server(s) are single point of failures that
more » ... breached, enable attackers to forge arbitrary tokens or mount offline dictionary attacks to recover client credentials. Our work is the first to introduce and formalize the notion of password-based threshold token-based authentication which distributes the role of an identity provider among n servers. Any t servers can collectively verify passwords and generate tokens, while no t − 1 servers can forge a valid token or mount offline dictionary attacks. We then introduce PASTA, a general framework that can be instantiated using any threshold token generation scheme, wherein clients can "sign-on" using a two-round (optimal) protocol that meets our strong notions of unforgeability and password-safety. We instantiate and implement our framework in C++ using two threshold message authentication codes (MAC) and two threshold digital signatures with different trade-offs. Our experiments show that the overhead of protecting secrets and credentials against breaches in PASTA, i.e. compared to a naïve single server solution, is extremely low (1-5%) in the most likely setting where client and servers communicate over the internet. The overhead is higher in case of MAC-based tokens over a LAN (though still only a few milliseconds) due to public-key operations in PASTA. We show, however, that this cost is inherent by proving a symmetric-key only solution impossible.
dblp:journals/iacr/AgrawalMMM18 fatcat:xqhthhajvbbxjhklzeq4r3alwy