The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Differential Privacy via Wavelet Transforms
Abstract
Privacy-preserving data publishing has attracted considerable research interest in recent years. Among the existing solutions, ∈-differential privacy provides the strongest privacy guarantee. Existing data publishing methods that achieve ∈-differential privacy, however, offer little data utility. In particular, if the output data set is used to answer count queries, the noise in the query answers can be proportional to the number of tuples in the data, which renders the results useless. In this paper, we develop a data publishing technique that ensures ∈-differential privacy while providing accurate answers for range-count queries, i.e., count queries where the predicate on each attribute is a range. The core of our solution is a framework that applies wavelet transforms on the data before adding noise to it. We present instantiations of the proposed framework for both ordinal and nominal data, and we provide a theoretical analysis on their privacy and utility guarantees. In an extensive experimental study on both real and synthetic data, we show the effectiveness and efficiency of our solution.
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Citations
Cites
Calibrating Noise to Sensitivity in Private Data Analysis
Dwork, C., McSherry, F., Nissim, K., & Smith, A. (2006) · Lecture Notes in Computer Science · doi:10.1007/11681878_14 · Cited by 7,272
Privacy-preserving data publishing: A survey of recent developments
Fung, B. C. M., Wang, K., Chen, R., & Yu, P. S. (2010) · ACM Computing Surveys · doi:10.1145/1749603.1749605 · Cited by 1,704 · cited 1×
“A plethora of techniques have been proposed for privacy-preserving data publishing (see [1], [2] for surveys).”
Background
Cited by
A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection
Li, Q., Wen, Z., Wu, Z., Hu, S., Wang, N., Li, Y., Liu, X., & He, B. (2023) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2021.3124599 · Cited by 1,407
Differential Privacy Techniques for Cyber Physical Systems: A Survey
Hassan, M. U., Rehmani, M. H., & Chen, J. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2019.2944748 · Cited by 564
Amplification by Shuffling: From Local to Central Differential Privacy via Anonymity
Erlingsson, Ú., Feldman, V., Mironov, I., Raghunathan, A., Talwar, K., & Thakurta, A. (2019) · Proceedings of the Thirtieth Annual ACM-SIAM Symposium on Discrete Algorithms · doi:10.1137/1.9781611975482.151 · Cited by 264
Enhancing data utility in differential privacy via microaggregation-based $$k$$ k -anonymity
Soria-Comas, J., Domingo-Ferrer, J., Sánchez, D., & Martínez, S. (2014) · The VLDB Journal · doi:10.1007/s00778-014-0351-4 · Cited by 149
A survey of local differential privacy for securing internet of vehicles
Zhao, P., Zhang, G., Wan, S., Liu, G., & Umer, T. (2020) · The Journal of Supercomputing · doi:10.1007/s11227-019-03104-0 · Cited by 66
SafePath: Differentially-private publishing of passenger trajectories in transportation systems
Al-Hussaeni, K., Fung, B. C. M., Iqbal, F., Dagher, G. G., & Park, E. G. (2018) · Computer Networks · doi:10.1016/j.comnet.2018.07.007 · Cited by 52
Privacy-Enhancing Technologies in Federated Learning for the Internet of Healthcare Things: A Survey
Mosaiyebzadeh, F., Pouriyeh, S., Parizi, R. M., Sheng, Q. Z., Han, M., Zhao, L., Sannino, G., Ranieri, C. M., Ueyama, J., & Batista, D. M. (2023) · Electronics · doi:10.3390/electronics12122703 · Cited by 51
Privacy-preserving aggregation of personal health data streams
Kim, J. W., Jang, B., & Yoo, H. (2018) · PLOS ONE · doi:10.1371/journal.pone.0207639 · Cited by 49
LLM-PBE: Assessing Data Privacy in Large Language Models
Li, Q., Hong, J., Xie, C., Tan, J., Xin, R., Hou, J., Yin, X., Wang, Z., Hendrycks, D., Wang, Z., et al. (2024) · Proceedings of the VLDB Endowment · doi:10.14778/3681954.3681994 · Cited by 42
On the Power of Multiple Anonymous Messages
Ghazi, B., Golowich, N., Kumar, R., Pagh, R., & Velingker, A. (2019) · arXiv · doi:10.48550/arxiv.1908.11358 · Cited by 26
Li, X., Wang, Y., Song, J., Liu, Y., Zhang, X., Zhou, K., & Li, C. (2020) · World Wide Web · doi:10.1007/s11280-019-00769-8 · Cited by 7
Duan, Y., Liu, J., Ming, X., Jin, W., Song, Z., & Peng, X. (2023) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-35678-0_3 · Cited by 3
Metadata
- Authors
- Xiao, X., Wang, G., & Gehrke, J.
- Venue
- IEEE Transactions on Knowledge and Data Engineering
- Published
- 2011-08
- Volume
- 23
- Issue
- 8
- Pages
- 1200-1214
- ISSN
- 1041-4347 (ISSN-L)
- OpenAlex
- W1990503647
- Cited by
- 369
- References
- 2
- FWCI
- 15.30
- Type
- journal article
- Language
- en
- Open access
- closed
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