Correlated network data publication via differential privacy
By Rui Chen, Benjamin C. M. Fung, Philip S. Yu, Bipin C. Desai. 2013
235 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Statistics and Probability, Transportation
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
- Prateek Mittal
- Bo Li
- Supriyo Chakraborty
- Changchang Liu
- Bowen Zhou
- Jinfeng Yi
- Muneeb Ul Hassan
- Jinjun Chen
In Statistics and Probability
In Transportation
- Prateek Mittal
- Supriyo Chakraborty
- Muneeb Ul Hassan
- Changchang Liu
- Jinjun Chen
- Mubashir Husain Rehmani
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].”
- Trustworthy AI: From Principles to Practices (2021, background): “DP has been explored in various data publishing tasks, such as log data [159, 385], set-valued data [76], correlated network data [75], and crowdsourced data [278, 344].”
- DEEProtect: Enabling Inference-based Access Control on Mobile Sensing Applications (2017, methodology): “According to the composition theorem of DP [60], we propose a baseline approach to achieve -LDP, which inserts a Laplacian noise to each temporal sensor data point with the same parameter of λ = dim(xt)∆Q/ 3, where dim(xt) is the dimension of each segmented sensor data xt (we refer interested reader”
- Differential Privacy Techniques for Cyber Physical Systems: A Survey (2018, background): “This type of communication is vulnerable to correlation attacks, in which the correlation between real-world data may reveal more than expected information [145], [166].”
- Differential Privacy for Deep and Federated Learning: A Survey (2022, unknown): “This approach [80] provides a higher privacy protection level and data availability compared to the approaches proposed in [107] and [108], however, the authors did not evaluate the impact of the proposed approach on the accuracy.”
- Correlated Differential Privacy: Hiding Information in Non-IID Data Set (2015, background): “Most existing work assumes that the dataset consists of independent records.”