FastPaD: A Fast Privacy-Preserving Password Similarity Leakage Detection Protocol for IoT Services

In the digital age of the Internet of Things, users rely extensively on online accounts to access a variety of IoT services and applications. However, password leakage significantly threatens users’ privacy, assets, and reputations, making passwords primary targets for cyberattacks, including credential-guessing attacks. To address this vulnerability, this paper proposes FastPaD, a Fast privacy-preserving protocol designed to detect password similarity leakage using homomorphic encryption. FastPaD employs a novelhorizontal homomorphic batch encoding method, facilitating efficient batch detection of similar passwords. The protocol also incorporates optimization strategies such aspolynomial partitioning and power computation windowing, significantly reducing the depth of homomorphic multiplications. This enables the use of smaller encryption parameters, which improves computational efficiency. Moreover, FastPaD features a customizable mechanism to balance functionality and performance, effectively optimizing the trade-off between communication and computational overhead. Experimental results demonstrate that in scenarios without similarity detection, FastPaD achieves a detection computation time of 0.17 seconds and a total communication overhead of 3.88 MB, surpassing state-of-the-art protocols. When similarity detection is enabled, FastPaD completes the detection process in 6.45 seconds with a communication overhead of 10.64 MB. Compared to the Pipa protocol, FastPaD provides approximately a 2.70× improvement in computation time and a 22.27× improvement in communication efficiency. Relative to Yu’s protocol, FastPaD demonstrates a 15.78× faster computation and a 1.31× reduction in communication overhead.