Relational databases are crucial for data publication and dissemination. Database watermarking techniques are applied for copyright protection. However, (i) Key exposure of existing watermarking schemes compromises security by making watermarks visible and vulnerable to removal. (ii) The watermarking scheme frameworks lack the flexibility for multi-party scenarios, restricting their ability to adapt watermark information for diverse tasks. In this paper, we present an asymmetric and robust watermarking scheme called TSALockMark. It supports a many-to-many configuration to allocate multiple keys, facilitates multi-party watermarking detection, supports ownership authentication and approval dissemination, and resists key exposure and malicious attacks. Moreover, we introduce the number control of the least significant bits and particle swarm optimization algorithm to preserve semantics and statistics. Experiments on three real-world datasets demonstrate the proposed scheme’s functionality, robustness, and performance, offering effective solutions to data dissemination challenges.

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TSALockMark: An Asymmetric and Robust Watermarking Scheme for Relational Databases with Distortion Constraints

  • Ke Yang,
  • Shuguang Yuan,
  • Jing Yu,
  • Zhaochen Li,
  • Yuyang Wang,
  • Chi Chen

摘要

Relational databases are crucial for data publication and dissemination. Database watermarking techniques are applied for copyright protection. However, (i) Key exposure of existing watermarking schemes compromises security by making watermarks visible and vulnerable to removal. (ii) The watermarking scheme frameworks lack the flexibility for multi-party scenarios, restricting their ability to adapt watermark information for diverse tasks. In this paper, we present an asymmetric and robust watermarking scheme called TSALockMark. It supports a many-to-many configuration to allocate multiple keys, facilitates multi-party watermarking detection, supports ownership authentication and approval dissemination, and resists key exposure and malicious attacks. Moreover, we introduce the number control of the least significant bits and particle swarm optimization algorithm to preserve semantics and statistics. Experiments on three real-world datasets demonstrate the proposed scheme’s functionality, robustness, and performance, offering effective solutions to data dissemination challenges.