Differentially Private Data Publishing and Analysis: A Survey

By Tianqing Zhu, Gang Li, Wanlei Zhou, Philip S. Yu. IEEE Transactions on Knowledge and Data Engineering, 2017

308 citations recorded by the source corpus.

Differential privacy is an essential and prevalent privacy model that has been widely explored in recent decades. This survey provides a comprehensive and structured overview of two research directions: differentially private data publishing and differentially private data analysis. We compare the diverse release mechanisms of differentially private data publishing given a variety of input data in terms of query type, the maximum number of queries, efficiency, and accuracy. We identify two basic frameworks for differentially private data analysis and list the typical algorithms used within each framework. The results are compared and discussed based on output accuracy and efficiency. Further, we propose several possible directions for future research and possible applications.

Subfields: Artificial Intelligence, Computer Science Applications

View Paper: https://www.semanticscholar.org/paper/2c7e95630b78e7dce553aa7c1eb8a937f4be7987

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

1. Kaixiang Lin
2. Ivor Wai-Hung Tsang
3. Yan Zhang
4. Sabita Maharjan
5. Qing-Long Han
6. Bo Han
7. Jun Zhang
8. Sheng Wen

In Computer Science Applications

1. Jiayu Zhou
2. Ying Zhao
3. Yan Zhang
4. Jinjun Chen
5. Xiaokui Xiao

Citations in the corpus

• Generating Differentially Private Datasets Using GANs (2018, unknown): “”public” data in one form or another (Abadi et al. 2016; Papernot et al. 2016; 2018; Zhang, Ji, and Wang 2018). On the other hand, existing data release solutions struggle with high-dimensional data (Zhu et al. 2017). It limits the choice of methods for comparison. We chose to compare learning perfo”
• A Survey on Differential Privacy for Unstructured Data Content (2022, methodology): “[122] 2017 Data release & data mining Anonymized and learning-based DP publishing, Laplace mechanism-based DP data analysis & private learning structure [16] 2018 LDP tutorial LDP deployments, fundamentals, statistical tasks [37] 2019 Decision trees DP decision tree algorithms, realistic implementat”
• Blockchain and Federated Learning for Privacy-Preserved Data Sharing in Industrial IoT (2020, background): “A machine learning differentially private [5] was proposed to publish data structures instead of publishing queries and answers directly, in the constraint of differential privacy.”
• Differential Privacy Techniques for Cyber Physical Systems: A Survey (2018, unknown): “differential privacy is presented in [42].”
• Differentially Private Generative Adversarial Network (2018, methodology): “earn from models trained by data from multiple sources with DP requirements (hence multiple level of noise). A comprehensive and structured overview of DP data publishing and analysis can be found in [37], where several possible future directions and possible applications are also mentioned. Differe”
• Federating Recommendations Using Differentially Private Prototypes (2020, methodology): “Nissim et al. (2007) and Wang et al. (2015) use a similar framework but calibrate the noise by local sensitivity, which is difficult to estimate without assumptions on the dataset (Zhu et al., 2017).”
• Software Vulnerability Detection Using Deep Neural Networks: A Survey (2020, background): “This has been proved by the “Heartbleed” [1] and “Shellshock” [3] vulnerabilities disclosed five years ago, and in 2017, the vulnerability in Apache Struts which resulted in 143 million consumers’ financial data to be compromised [5], [15], [125], [126].”
• Privacy-Preserving Stochastic Gradual Learning (2018, methodology): “To handle large-scale sensitive data, it is reasonable to propose the privacypreserving stochastic optimizations [18] under the global privacy [19,20,21].”