Correlated Differential Privacy: Hiding Information in Non-IID Data Set
By Tianqing Zhu, Ping Xiong, Gang Li, Wanlei Zhou. 2015
198 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Electrical and Electronic Engineering
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Hamed Haddadi
- Andrea Cavallaro
- Mohammad Malekzadeh
- Richard G. Clegg
- Philip S. Yu
- Changchang Liu
- Supriyo Chakraborty
- Prateek Mittal
In Electrical and Electronic Engineering
- Prateek Mittal
- Supriyo Chakraborty
- Changchang Liu
- Muneeb Ul Hassan
- Jinjun Chen
- Jun Zhao
- Philip S. Yu
- Richard G. Clegg
Citations in the corpus
- Dependence Makes You Vulnberable: Differential Privacy Under Dependent Tuples (2016, background): “The fact that dependence (or correlation) among tuples can degrade the expected privacy guarantees of DP mechanisms was first observed by Kifer et al. [24], and later in [8], [21], [25], [40].”
- Privacy and Utility Preserving Sensor-Data Transformations (2019, background): “Because an independent and identically distributed noise can be easily removed from correlated time-series [18], to reduce the risk of information leakage, the correlation between noise and original time-series should be indistinguishable [19].”
- Differential Privacy Techniques for Cyber Physical Systems: A Survey (2018, background): “The noise in these techniques is added in such a way that it minimizes rate of error and improves utility of data, along with satisfying ε -differential privacy value; ( ii ) Correlation Exploitation [60], [67] [79]–[81], in these proposed strategies of differential privacy, the correlation among at”
- $\textsf{LoPub}$ : High-Dimensional Crowdsourced Data Publication With Local Differential Privacy (2016, background): “In existing work [18], [32] on homogeneous data, correlations can be simply captured by distance or similarity metrics [36].”
- Locally Differentially Private Data Collection and Analysis (2019, background): “Since the introduction of differential privacy (DP), a large number of DP-based mechanisms [27, 25, 57] have been proposed and applied to numerous scenarios, such as data statistics [54, 61, 11], learning models [1, 40, 59, 38], and systems [29, 6].”