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Private, efficient, and flexible: protecting names based on message-derived encryption in named data networking

Kai He, Shengyuan Shi, Chunxiao Yin, Hongyan Wan, Jiaoli Shi · Cybersecurity · 2025

Abstract Named data networking (NDN) is considered a novel architecture of the next-generation Internet that delivers content by names. However, the human-readable name may potentially leak users’ privacy. Existing solutions have focused on encrypting to protect privacy, but they are neither efficient in the case of one publisher and multiple subscribers nor successful in supporting prefix matching. To address the above challenges, we propose an efficient and flexible name scheme with privacy preservation, which combines message-driven encryption with Bloom Filter. Firstly, message-driven encryption is utilized to protect name privacy, thus supporting efficient and secure encrypted name matching. Secondly, each name is divided into multiple components, and then each component is encrypted separately to support flexible prefix matching. Lastly, the technology of Bloom Filter with random numbers is explored to improve the efficiency and accuracy of name matching. Security and performance analysis shows that the proposed scheme effectively enhances the efficiency and accuracy of data matching while protecting name privacy.

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