PrivTrie: Effective Frequent Term Discovery under Local Differential Privacy

By Ning Wang, Xiaokui Xiao, Y. Yang, Ta Duy Hoang, Hyejin Shin, Junbum Shin, Ge Yu. 2018

86 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Management Science and Operations Research

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. Ninghui Li
2. Tianhao Wang
3. Somesh Jha
4. Jun Zhao
5. Xiaokui Xiao
6. Y. Yang
7. Ge Yu
8. Boris Skoric

In Management Science and Operations Research

1. Jun Zhao
2. Siu Cheung Hui
3. Zibin Zheng
4. Ge Yu
5. Somesh Jha
6. Ninghui Li
7. Hyejin Shin
8. Ning Wang

Citations in the corpus

• Collecting and Analyzing Multidimensional Data with Local Differential Privacy (2019, background): “Various data analytics and machine learning problems have been studied under LDP, such as probability distribution estimation [15], [23], [30], [32], [42], heavy hitter discovery [4], [7], [33], [39], frequent new term discovery [37], frequency estimation [5], [38], frequent itemset mining [40], mar”
• Blockchain for Internet of Things: A Survey (2019, background): “Nevertheless, the decryption process is often time-consuming thereby resulting in the inefficiency of data analytics [123].”
• Consistent and Accurate Frequency Oracles under Local Differential Privacy (2019, methodology): “In the LDP setting, [32] and [23] also consider the hierarchy structure and apply the technique of [18].”
• Locally Differentially Private Heavy Hitter Identification (2017, methodology): “We also compare with PrivTrie [33] as described in Section 4.”