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The (generative) artificial intelligence (AI) era has profoundly reshaped the meaning and value of data.
C. E. Shannon, “Communication theory of secrecy systems,”
1949
Earlier work this paper cites.
W. Diffie and M. E. Hellman, “New directions in cryptography,”
1976
Earlier work this paper cites.
R. C. Merkle, “A digital signature based on a conventional encryption function,” in
1987
Earlier work this paper cites.
R. L. Rivest, “The md4 message digest algorithm,” in
1991
Earlier work this paper cites.
F. Hartung and M. Kutter, “Multimedia watermarking techniques,”
1999
Earlier work this paper cites.
P. Paillier, “Public-key cryptosystems based on composite degree residuosity classes,” in
1999
Earlier work this paper cites.
D. Boneh and M. Franklin, “Identity-based encryption from the weil pairing,” in
2001
Earlier work this paper cites.
L. Sweeney, “k-anonymity: A model for protecting privacy,”
2002
Earlier work this paper cites.
C. Dwork, F. McSherry, K. Nissim, and A. Smith, “Calibrating noise to sensitivity in private data analysis,” in
2006
Earlier work this paper cites.
A. Machanavajjhala, D. Kifer, J. Gehrke, and M. Venkitasubramaniam, “l-diversity: Privacy beyond k-anonymity,”
2007
Earlier work this paper cites.
S. R. Ganta, S. P. Kasiviswanathan, and A. Smith, “Composition attacks and auxiliary information in data privacy,” in
2008
Earlier work this paper cites.
X. Zhang and S. Wang, “Fragile watermarking with error-free restoration capability,”
2008
Earlier work this paper cites.
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “Imagenet: A large-scale hierarchical image database,” in
2009
Earlier work this paper cites.
S. Yu, C. Wang, K. Ren, and W. Lou, “Achieving secure, scalable, and fine-grained data access control in cloud computing,” in
2010
Earlier work this paper cites.
J. N. Weinstein, E. A. Collisson, G. B. Mills, K. R. Shaw, B. A. Ozenberger, K. Ellrott, I. Shmulevich, C. Sander, and J. M. Stuart, “The cancer genome atlas pan-cancer analysis project,”
2013
Earlier work this paper cites.
K. Yang, X. Jia, K. Ren, B. Zhang, and R. Xie, “Dac-macs: Effective data access control for multiauthority cloud storage systems,”
2013
Earlier work this paper cites.
G. Hinton, O. Vinyals, and J. Dean, “Distilling the knowledge in a neural network,” in
2014
Earlier work this paper cites.
Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,”
2015
Earlier work this paper cites.
R. Shokri and V. Shmatikov, “Privacy-preserving deep learning,” in
2015
Earlier work this paper cites.
E. Union, “General data protection regulation,” 2016, eU. [Online]. Available:
2016
Earlier work this paper cites.
M. Abadi, A. Chu, I. Goodfellow, H. B. McMahan, I. Mironov, K. Talwar, and L. Zhang, “Deep learning with differential privacy,” in
2016
Earlier work this paper cites.
C. A. of China, “Cybersecurity law of the prc,” 2016, china. [Online]. Available:
2016
Earlier work this paper cites.
P. Martins, L. Sousa, and A. Mariano, “A survey on fully homomorphic encryption: An engineering perspective,”
2017
Earlier work this paper cites.
B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas, “Communication-efficient learning of deep networks from decentralized data,” in
2017
Earlier work this paper cites.
R. Shokri, M. Stronati, C. Song, and V. Shmatikov, “Membership inference attacks against machine learning models,” in
2017
Earlier work this paper cites.
C. Finn, P. Abbeel, and S. Levine, “Model-agnostic meta-learning for fast adaptation of deep networks,” in
2017
Earlier work this paper cites.
O. Purcell, J. Wang, P. Siuti, and T. K. Lu, “Encryption and steganography of synthetic gene circuits,”
2018
Earlier work this paper cites.
J. Jordon, J. Yoon, and M. Van Der Schaar, “Pate-gan: Generating synthetic data with differential privacy guarantees,” in
2018
Earlier work this paper cites.
O. Gupta and R. Raskar, “Distributed learning of deep neural network over multiple agents,”
2018
Earlier work this paper cites.
C. S. Legislature, “California consumer privacy act,” 2018, uSA. [Online]. Available:
2018
Earlier work this paper cites.
E. Commission, “Ethics guidelines for trustworthy ai,” 2019, eU. [Online]. Available:
2019
Earlier work this paper cites.
A. Ministry, “Artificial intelligence mission austria 2030,” 2019, austria. [Online]. Available:
2019
Earlier work this paper cites.
H. Deng, Z. Qin, Q. Wu, Z. Guan, R. H. Deng, Y. Wang, and Y. Zhou, “Identity-based encryption transformation for flexible sharing of encrypted data in public cloud,”
2020
Earlier work this paper cites.
J. Ho, A. Jain, and P. Abbeel, “Denoising diffusion probabilistic models,” in
2020
Earlier work this paper cites.
P. Lewis, E. Perez, A. Piktus, F. Petroni, V. Karpukhin, N. Goyal, H. Küttler, M. Lewis, W.-t. Yih, T. Rocktäschel
2020
Earlier work this paper cites.
E. J. De Aguiar, B. S. Faiçal, B. Krishnamachari, and J. Ueyama, “A survey of blockchain-based strategies for healthcare,”
2020
Earlier work this paper cites.
C. Guo, T. Goldstein, A. Hannun, and L. Van Der Maaten, “Certified data removal from machine learning models,” in
2020
Earlier work this paper cites.
X. Jia, X. Wei, X. Cao, and X. Han, “Adv-watermark: A novel watermark perturbation for adversarial examples,” in
2020
Earlier work this paper cites.
H. Huang, X. Ma, S. M. Erfani, J. Bailey, and Y. Wang, “Unlearnable examples: Making personal data unexploitable,” in
2021
Earlier work this paper cites.
B. Liu, M. Ding, S. Shaham, W. Rahayu, F. Farokhi, and Z. Lin, “When machine learning meets privacy: A survey and outlook,”
2021
Earlier work this paper cites.
D. Han, Y. Zhu, D. Li, W. Liang, A. Souri, and K.-C. Li, “A blockchain-based auditable access control system for private data in service-centric iot environments,”
2021
Earlier work this paper cites.
J. Gou, B. Yu, S. J. Maybank, and D. Tao, “Knowledge distillation: A survey,”
2021
Earlier work this paper cites.
C. Meurisch and M. Mühlhäuser, “Data protection in ai services: A survey,”
2021
Earlier work this paper cites.
L. Bourtoule, V. Chandrasekaran, C. A. Choquette-Choo, H. Jia, A. Travers, B. Zhang, D. Lie, and N. Papernot, “Machine unlearning,” in
2021
Earlier work this paper cites.
L. Wang, S. Xu, R. Xu, X. Wang, and Q. Zhu, “Non-transferable learning: A new approach for model ownership verification and applicability authorization,” in
2021
Earlier work this paper cites.
M. Botta, D. Cavagnino, and R. Esposito, “Neunac: A novel fragile watermarking algorithm for integrity protection of neural networks,”
2021
Earlier work this paper cites.
X. Cao, J. Jia, and N. Z. Gong, “Ipguard: Protecting intellectual property of deep neural networks via fingerprinting the classification boundary,” in
2021
Earlier work this paper cites.
X. Guo, M. A. Khalid, I. Domingos, A. L. Michala, M. Adriko, C. Rowel, D. Ajambo, A. Garrett, S. Kar, X. Yan
2021
Cited alongside, same era.
T. S. C. of the National People’s Congress, “Data security law of the people’s republic of china,” 2021. [Online]. Available:
2021
Cited alongside, same era.
G. of the People’s Republic of China, “Personal information protection law of the prc,” 2021, china. [Online]. Available:
2021
Cited alongside, same era.
N. Mehrabi, F. Morstatter, N. Saxena, K. Lerman, and A. Galstyan, “A survey on bias and fairness in machine learning,”
2021
Cited alongside, same era.
N. Carlini, F. Tramer, E. Wallace, M. Jagielski, A. Herbert-Voss, K. Lee, A. Roberts, T. B. Brown, D. Song, U. Erlingsson, A. Oprea, and C. Raffel, “Extracting training data from large language models,” in
2021
Cited alongside, same era.
2024
Later among the works it cites.
2024
Later among the works it cites.
S. Abdali, R. Anarfi, C. Barberan, and J. He, “Decoding the ai pen: Techniques and challenges in detecting ai-generated text,” in
2024
Later among the works it cites.
X. Li, K. Li, Y. Zheng, C. Yan, X. Ji, and W. Xu, “Safeear: Content privacy-preserving audio deepfake detection,” in
2024
Later among the works it cites.
F. Liu, H. Luo, Y. Li, P. Torr, and J. Gu, “Which model generated this image? a model-agnostic approach for origin attribution,” in
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W. Liang, G. A. Tadesse, D. Ho, L. Fei-Fei, M. Zaharia, C. Zhang, and J. Zou, “Advances, challenges and opportunities in creating data for trustworthy ai,”
2022
Cited alongside, same era.
Z. Guan, J. Jing, X. Deng, M. Xu, L. Jiang, Z. Zhang, and Y. Li, “Deepmih: Deep invertible network for multiple image hiding,”
2022
Cited alongside, same era.
Y. Li, Y. Bai, Y. Jiang, Y. Yang, S.-T. Xia, and B. Li, “Untargeted backdoor watermark: Towards harmless and stealthy dataset copyright protection,” in
2022
Cited alongside, same era.
Z. Huang, W.-j. Lu, C. Hong, and J. Ding, “Cheetah: Lean and fast secure two-party deep neural network inference,” in
2022
Cited alongside, same era.
Y. Li, Y. Jiang, Z. Li, and S.-T. Xia, “Backdoor learning: A survey,”
2022
Cited alongside, same era.
G. Ren, J. Wu, G. Li, S. Li, and M. Guizani, “Protecting intellectual property with reliable availability of learning models in ai-based cybersecurity services,”
2022
Cited alongside, same era.
Y. Li, L. Zhu, X. Jia, Y. Jiang, S.-T. Xia, and X. Cao, “Defending against model stealing via verifying embedded external features,” in
2022
Cited alongside, same era.
2024
Later among the works it cites.
G. Ren, G. Li, S. Li, L. Chen, and K. Ren, “Activedaemon: Unconscious dnn dormancy and waking up via user-specific invisible token,” in
2024
Later among the works it cites.
Z. Hong, Z. Wang, L. Shen, Y. Yao, Z. Huang, S. Chen, C. Yang, M. Gong, and T. Liu, “Improving non-transferable representation learning by harnessing content and style,” in
2024
Later among the works it cites.
X. Li, W. Liu, J. Lou, Y. Hong, L. Zhang, Z. Qin, and K. Ren, “Local differentially private heavy hitter detection in data streams with bounded memory,”
2024
Later among the works it cites.
J. Guo, Y. Li, L. Wang, S.-T. Xia, H. Huang, C. Liu, and B. Li, “Domain watermark: Effective and harmless dataset copyright protection is closed at hand,” in
2024
Later among the works it cites.
C. Wei, Y. Wang, K. Gao, S. Shao, Y. Li, Z. Wang, and Z. Qin, “Pointncbw: Towards dataset ownership verification for point clouds via negative clean-label backdoor watermark,”
2024
Later among the works it cites.
S. Shao, W. Yang, H. Gu, Z. Qin, L. Fan, Q. Yang, and K. Ren, “Fedtracker: Furnishing ownership verification and traceability for federated learning model,”
2024
Later among the works it cites.
Y. Hu, J. Lou, J. Liu, W. Ni, F. Lin, Z. Qin, and K. Ren, “Eraser: Machine unlearning in mlaas via an inference serving-aware approach,” in
2024
Later among the works it cites.
J. Jia, J. Liu, Y. Zhang, P. Ram, N. B. Angel, and S. Liu, “Wagle: Strategic weight attribution for effective and modular unlearning in large language models,” in
2024
Later among the works it cites.
Z. T. Z. D. S. W. Group, “Federal zero trust data security guide,” 2024. [Online]. Available:
2024
Later among the works it cites.
N. D. Administration, “Action plan of the development of trustworthy data space,” 2024, china. [Online]. Available:
2024
Later among the works it cites.
X. Li, Y. Yang, J. Deng, C. Yan, Y. Chen, X. Ji, and W. Xu, “Safegen: Mitigating sexually explicit content generation in text-to-image models,” in
2024
Later among the works it cites.
T. Wang, X. Liao, K. P. Chow, X. Lin, and Y. Wang, “Deepfake detection: A comprehensive survey from the reliability perspective,”
2024
Later among the works it cites.
P.-Y. Chen and S. Liu,
2025
Closest in time.
M. Team, “Midjourney,” Artificial intelligence image generation tool, 2023, accessed: 2025-05-13. [Online]. Available:
2025
Closest in time.
D. Guo, D. Yang, H. Zhang, J. Song, R. Zhang, R. Xu, Q. Zhu, S. Ma, P. Wang, X. Bi
2025
Closest in time.
Y. He, H. She, X. Qian, X. Zheng, Z. Chen, Z. Qin, and L. Cavallaro, “On benchmarking code llms for android malware analysis,” in
2025
Closest in time.
S. Longpre, N. Singh, M. Cherep, K. Tiwary, J. Materzynska, W. Brannon, R. Mahari, N. Obeng-Marnu, M. Dey, M. Hamdy
2025
Closest in time.
L. Du, X. Zhou, M. Chen, C. Zhang, Z. Su, P. Cheng, J. Chen, and Z. Zhang, “Sok: Dataset copyright auditing in machine learning systems,” in
2025
Closest in time.
B. Li, Y. Wei, Y. Fu, Z. Wang, Y. Li, J. Zhang, R. Wang, and T. Zhang, “Towards reliable verification of unauthorized data usage in personalized text-to-image diffusion models,” in
2025
Closest in time.
C. Zhu, J. Tang, J. M. Galjaard, P.-Y. Chen, R. Birke, C. Bos, L. Y. Chen
2025
Closest in time.
M. Yuksekgonul, F. Bianchi, J. Boen, S. Liu, P. Lu, Z. Huang, C. Guestrin, and J. Zou, “Optimizing generative ai by backpropagating language model feedback,”
2025
Closest in time.
Y. Li, L. Zhu, X. Jia, Y. Bai, Y. Jiang, S.-T. Xia, X. Cao, and K. Ren, “Move: Effective and harmless ownership verification via embedded external features,”
2025
Closest in time.
S. Shao, Y. Li, H. Yao, Y. He, Z. Qin, and K. Ren, “Explanation as a watermark: Towards harmless and multi-bit model ownership verification via watermarking feature attribution,” in
2025
Closest in time.
Z. Wang, J. Guo, J. Zhu, Y. Li, H. Huang, M. Chen, and Z. Tu, “Sleepermark: Towards robust watermark against fine-tuning text-to-image diffusion models,” in
2025
Closest in time.
J. Zhang, X. Yang, L. He, K. Chen, W.-j. Lu, Y. Wang, X. Hou, J. Liu, K. Ren, and X. Yang, “Secure transformer inference made non-interactive,” in
2025
Closest in time.
W. Qu, Y. Zhou, Y. Wu, T. Xiao, B. Yuan, Y. Li, and J. Zhang, “Prompt inversion attack against collaborative inference of large language models,” in
2025
Closest in time.
R. Haase, “Towards transparency and knowledge exchange in ai-assisted data analysis code generation,”
2025
Closest in time.
X. Zhao, S. Gunn, M. Christ, J. Fairoze, A. Fabrega, N. Carlini, S. Garg, S. Hong, M. Nasr, F. Tramer
2025
Closest in time.
X. Li, P.-Y. Chen, and W. Wei, “Where are we in audio deepfake detection? a systematic analysis over generative and detection models,”
2025
Closest in time.
D. A. Alber, Z. Yang, A. Alyakin, E. Yang, S. Rai, A. A. Valliani, J. Zhang, G. R. Rosenbaum, A. K. Amend-Thomas, D. B. Kurland
2025
Closest in time.
Y. Chen, S. Shao, E. Huang, Y. Li, P.-Y. Chen, Z. Qin, and K. Ren, “Refine: Inversion-free backdoor defense via model reprogramming,” in
2025
Closest in time.
T. Hanser, E. Ahlberg, A. Amberg, L. T. Anger, C. Barber, R. J. Brennan, A. Brigo, A. Delaunois, S. Glowienke, N. Greene
2025
Closest in time.
Y. He, B. Li, L. Liu, Z. Ba, W. Dong, Y. Li, Z. Qin, K. Ren, and C. Chen, “Towards label-only membership inference attack against pre-trained large language models,” in
2025
Closest in time.
D. Pasquini, E. M. Kornaropoulos, and G. Ateniese, “Llmmap: Fingerprinting for large language models,” in
2025
Closest in time.
E. Union, “General-purpose ai code of practice (draft),” 2025, eU; Found via news article on computing.co.uk. [Online]. Available:
2025
Closest in time.
N. Development and R. Commission, “Implementation plan on improving data circulation security governance to better promote the marketization and valorization of data elements,” 2025, china. [Online]. Available:
2025
Closest in time.
C. A. of China, “Methods for identifying synthetic content generated by artificial intelligence,” 2025, china. [Online]. Available:
2025
Closest in time.
OECD, “Enhancing Access to and Sharing of Data in the Age of Artificial Intelligence,” 2025, [Online; accessed date]. [Online]. Available:
2025
Closest in time.
O. of the Australian Information Commissioner, “Joint statement on building trustworthy data governance frameworks to encourage development of innovative and privacy-protective ai,” 2025, joint Statement. [Online]. Available:
2025
Closest in time.
2025
Closest in time.
Q. Zhang, H. Qiu, D. Wang, Y. Li, T. Zhang, W. Zhu, H. Weng, L. Yan, and C. Zhang, “A benchmark for semantic sensitive information in llms outputs,” in
2025
Closest in time.