Privacy preserving framework for brute force attacks in cloud environment

2017 ◽  
Vol 10 (1/2) ◽  
pp. 91
Author(s):  
Ambika Vishal Pawar ◽  
Ajay R. Dani
Keyword(s):  
Privacy Preserving ◽  
Brute Force ◽  
Cloud Environment

Privacy Preserving in Cloud Environment for Obstructed Shortest Path Query

2017 ◽  
Vol 96 (2) ◽  
pp. 2305-2322 ◽  
Author(s):  
Lei Zhang ◽  
Jing Li ◽  
Songtao Yang ◽  
Bin Wang
Keyword(s):  
Shortest Path ◽  
Privacy Preserving ◽  
Cloud Environment ◽  
Shortest Path Query

Privacy-Preserving Tensor Decomposition Over Encrypted Data in a Federated Cloud Environment

2020 ◽  
Vol 17 (4) ◽  
pp. 857-868 ◽  
Author(s):  
Jun Feng ◽  
Laurence T. Yang ◽  
Qing Zhu ◽  
Kim-Kwang Raymond Choo
Keyword(s):  
Tensor Decomposition ◽  
Privacy Preserving ◽  
Cloud Environment ◽  
Encrypted Data

Toward Responsible AI: An Overview of Federated Learning for User-centered Privacy-preserving Computing

2021 ◽  
Vol 11 (3-4) ◽  
pp. 1-22
Author(s):  
Qiang Yang
Keyword(s):  
Private Information ◽  
High Performance ◽  
Privacy Preserving ◽  
Security And Privacy ◽  
Brute Force ◽  
New Paradigm ◽  
User Privacy ◽  
Data Governance ◽  
Incentive Mechanisms ◽  
Rule Out

With the rapid advances of Artificial Intelligence (AI) technologies and applications, an increasing concern is on the development and application of responsible AI technologies. Building AI technologies or machine-learning models often requires massive amounts of data, which may include sensitive, user private information to be collected from different sites or countries. Privacy, security, and data governance constraints rule out a brute force process in the acquisition and integration of these data. It is thus a serious challenge to protect user privacy while achieving high-performance models. This article reviews recent progress of federated learning in addressing this challenge in the context of privacy-preserving computing. Federated learning allows global AI models to be trained and used among multiple decentralized data sources with high security and privacy guarantees, as well as sound incentive mechanisms. This article presents the background, motivations, definitions, architectures, and applications of federated learning as a new paradigm for building privacy-preserving, responsible AI ecosystems.

Implementation of a Privacy Preserving and Loss Data Retrieval System for Cloud Environment

2020 ◽  
Author(s):  
Satti Rami Reddy ◽  
Satti Mouli Satti Reddy ◽  
Suja Cherukullapurath Mana ◽  
B.Keerthi Samhitha ◽  
Jithina Jose
Keyword(s):  
Retrieval System ◽  
Data Retrieval ◽  
Privacy Preserving ◽  
Cloud Environment ◽  
Loss Data

Secure-aware and privacy-preserving electronic health record searching in cloud environment

2019 ◽  
Vol 32 (8) ◽  
pp. e3925 ◽  
Author(s):  
Xiao Wang ◽  
Aiqing Zhang ◽  
Xiaojuan Xie ◽  
Xinrong Ye
Keyword(s):  
Electronic Health Record ◽  
Privacy Preserving ◽  
Health Record ◽  
Cloud Environment ◽  
Electronic Health
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