Privacy-Preserving Inter-database Operations

By Gang Liang, Sudarshan S. Chawathe. 2004

66 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

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. Yang Liu
2. Jiankun Hu
3. Yanming Zhu
4. Xuefei Yin
5. Qiang Yang
6. Tianjian Chen
7. Yan Kang
8. Tao Fan

In Information Systems

1. Tianjian Chen
2. Yan Kang
3. Qiang Yang
4. Jiankun Hu
5. Yanming Zhu
6. Kewei Cheng
7. Yang Liu
8. Xuefei Yin

Citations in the corpus

• Federated Machine Learning (2019, methodology): “Since the user groups of the two companies are not the same, the system uses the encryption-based user ID alignment techniques such as [38, 56] to confirm the common users of both parties without A and B exposing their respective data.”
• SecureBoost: A Lossless Federated Learning Framework (2019, methodology): “In particular, we align the data samples under an encryption scheme by using the privacy-preserving protocol for inter-database intersections (Liang and Chawathe 2004).”
• A Comprehensive Survey of Privacy-preserving Federated Learning (2021, methodology): “An ID alignment technique such as in Reference [104] is first used to confirm the common IDs between C1 and C2.”
• Vertical Federated Learning: Concepts, Advances, and Challenges (2022, background): “PSI protocols can be realized using various techniques, such as encryption and signature strategies [96] and oblivious transfer [97, 98] etc.”