Differential Privacy in Telco Big Data Platform

By Xueyang Hu, Mingxuan Yuan, Jianguo Yao, Yu Deng, Lei Chen, Qiang Yang, Haibing Guan, Jia Zeng. 2015

63 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, 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

1. Jun Zhao
2. Bolin Ding
3. Xinyu Yang
4. Xuebin Ren
5. Ivor Wai-Hung Tsang
6. Teng Wang
7. Bo Han
8. Janardhan Kulkarni

In Computer Networks and Communications

1. Jun Zhao
2. Dawn Song
3. Bolin Ding
4. Sergey Yekhanin
5. Xinyu Yang
6. Mubashir Husain Rehmani
7. Janardhan Kulkarni
8. Noah M. Johnson

In Electrical and Electronic Engineering

1. Muneeb Ul Hassan
2. Jinjun Chen
3. Jun Zhao
4. Dawn Song
5. Bolin Ding
6. Sergey Yekhanin
7. Ivor Wai-Hung Tsang
8. Xinyu Yang

Citations in the corpus

• Collecting Telemetry Data Privately (2017, background): “cy [12] (DP) has emerged as defacto standard for the privacy guarantees. In the context of telemetry collection one typically considers algorithms that exhibit differential privacy in the local model [16, 22, 18, 8, 5, 3, 25], also called randomized response model [26], -amplification [17], or FRAPP ”
• 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].”
• Principled Evaluation of Differentially Private Algorithms using DPBench (2015, unknown): “, [13]) use only these standard techniques.”
• Practical Differential Privacy for SQL Queries Using Elastic Sensitivity (2017, background): “Recent work has evaluated differential privacy mechanisms on real-world data [11,23,24], however this work uses a limited set of queries representing a single, special-purpose analytics task such as histogram analysis [11] or range queries [23].”
• Differential Privacy Techniques for Cyber Physical Systems: A Survey (2018, background): “Differential privacy has the potential to provide substantial amount of privacy to majority of application of CPSs and it may be a good choice where critical or public information needs to be preserved [139].”
• Towards Practical Differential Privacy for SQL Queries (2017, background): “In contrast with previous evaluations of differential privacy [15, 32, 33], our dataset includes a wide variety of real queries executed on real data.”
• Privacy-Preserving Stochastic Gradual Learning (2018, background): “Nevertheless, all their works are based on global differential privacy [36].”