Biblio

Most blockchain-based identity authentication systems focus on using blockchain to establish the public key infrastructure (PKI). It can solve the problem of single point of failure and certificate transparency faced by traditional PKI systems, but there are still some problems such as complex certificate management and complex certificate usage process. In this paper, we propose an identity authentication scheme based on blockchain and identity-based cryptography (IBC). The scheme implements a decentralized private key generator (PKG) by deploying the smart contract in Ethereum blockchain, and uses the IBC signature algorithm and challenge-response protocol during the authentication process. Compared with other blockchain-based identity authentication systems, the scheme not only prevents the single point of failure, but also avoids the complex certificate management, has lower system complexity, and resists impersonation attack, man-in-the-middle attack and replay attack.

Two-factor authentication (2FA) popularly works by verifying something the user knows (a password) and something she possesses (a token, popularly instantiated with a smart phone). Conventional 2FA systems require extra interaction like typing a verification code, which is not very user-friendly. For improved user experience, recent work aims at zero-effort 2FA, in which a smart phone placed close to a computer (where the user enters her username/password into a browser to log into a server) automatically assists with the authentication. To prove her possession of the smart phone, the user needs to prove the phone is on the login spot, which reduces zero-effort 2FA to co-presence detection. In this paper, we propose SoundAuth, a secure zero-effort 2FA mechanism based on (two kinds of) ambient audio signals. SoundAuth looks for signs of proximity by having the browser and the smart phone compare both their surrounding sounds and certain unpredictable near-ultrasounds; if significant distinguishability is found, SoundAuth rejects the login request. For the ambient signals comparison, we regard it as a classification problem and employ a machine learning technique to analyze the audio signals. Experiments with real login attempts show that SoundAuth not only is comparable to existent schemes concerning utility, but also outperforms them in terms of resilience to attacks. SoundAuth can be easily deployed as it is readily supported by most smart phones and major browsers.