Zobrazeno 1 - 10
of 371
pro vyhledávání: '"Li Ninghui"'
Autor:
Zhao, Joshua C., Bagchi, Saurabh, Avestimehr, Salman, Chan, Kevin S., Chaterji, Somali, Dimitriadis, Dimitris, Li, Jiacheng, Li, Ninghui, Nourian, Arash, Roth, Holger R.
Deep learning has shown incredible potential across a vast array of tasks and accompanying this growth has been an insatiable appetite for data. However, a large amount of data needed for enabling deep learning is stored on personal devices and recen
Externí odkaz:
http://arxiv.org/abs/2405.03636
Recent studies reveal that local differential privacy (LDP) protocols are vulnerable to data poisoning attacks where an attacker can manipulate the final estimate on the server by leveraging the characteristics of LDP and sending carefully crafted da
Externí odkaz:
http://arxiv.org/abs/2403.19510
Autor:
Du, Yuntao, Li, Ninghui
Data synthesis has been advocated as an important approach for utilizing data while protecting data privacy. A large number of tabular data synthesis algorithms (which we call synthesizers) have been proposed. Some synthesizers satisfy Differential P
Externí odkaz:
http://arxiv.org/abs/2402.06806
In Member Inference (MI) attacks, the adversary try to determine whether an instance is used to train a machine learning (ML) model. MI attacks are a major privacy concern when using private data to train ML models. Most MI attacks in the literature
Externí odkaz:
http://arxiv.org/abs/2311.00919
Publikováno v:
E3S Web of Conferences, Vol 261, p 02085 (2021)
This paper discusses the influence of pH value, temperature and current density on the crystalline and amorphous structure of Ni-W alloy coatings during the electroplating process. The relationship between the corrosion resistance of Ni-W alloy coati
Externí odkaz:
https://doaj.org/article/a4403e7d26f84e4880fb281017981092
Autor:
Lee, Yu-Tsung, Chen, Haining, Enck, William, Vijayakumar, Hayawardh, Li, Ninghui, Qian, Zhiyun, Petracca, Giuseppe, Jaeger, Trent
Android's filesystem access control is a crucial aspect of its system integrity. It utilizes a combination of mandatory access controls, such as SELinux, and discretionary access controls, like Unix permissions, along with specialized access controls
Externí odkaz:
http://arxiv.org/abs/2302.13506
Publikováno v:
PVLDB, 16(6): 1277 - 1290, 2023
In many applications, multiple parties have private data regarding the same set of users but on disjoint sets of attributes, and a server wants to leverage the data to train a model. To enable model learning while protecting the privacy of the data s
Externí odkaz:
http://arxiv.org/abs/2208.01700
Although local differential privacy (LDP) protects individual users' data from inference by an untrusted data curator, recent studies show that an attacker can launch a data poisoning attack from the user side to inject carefully-crafted bogus data i
Externí odkaz:
http://arxiv.org/abs/2205.11782
Autor:
Xiong, Aiping, Wu, Chuhao, Wang, Tianhao, Proctor, Robert W., Blocki, Jeremiah, Li, Ninghui, Jha, Somesh
Proper communication is key to the adoption and implementation of differential privacy (DP). However, a prior study found that laypeople did not understand the data perturbation processes of DP and how DP noise protects their sensitive personal infor
Externí odkaz:
http://arxiv.org/abs/2202.10014