5,886 research outputs found
A Hybrid Multi-user Cloud Access Control based Block Chain Framework for Privacy Preserving Distributed Databases
Most of the traditional medical applications are insecure and difficult to compute the data integrity with variable hash size. Traditional medical data security systems are insecure and it depend on static parameters for data security. Also, distributed based cloud storage systems are independent of integrity computational and data security due to unstructured data and computational memory. As the size of the data and its dimensions are increasing in the public and private cloud servers, it is difficult to provide the machine learning based privacy preserving in cloud computing environment. Block-chain technology plays a vital role for large cloud databases. Most of the conventional block-chain frameworks are based on the existing integrity and confidentiality models. Also, these models are based on the data size and file format. In this model, a novel integrity verification and encryption framework is designed and implemented in cloud environment. In order to overcome these problems in the cloud computing environment, a hybrid integrity and security-based block-chain framework is designed and implemented on the large distributed databases. In this framework,a novel decision tree classifier is used along with non-linear mathematical hash algorithm and advanced attribute-based encryption models are used to improve the privacy of multiple users on the large cloud datasets. Experimental results proved that the proposed advanced privacy preserving based block-chain technology has better efficiency than the traditional block-chain based privacy preserving systems on large distributed databases
Privacy and Robustness in Federated Learning: Attacks and Defenses
As data are increasingly being stored in different silos and societies
becoming more aware of data privacy issues, the traditional centralized
training of artificial intelligence (AI) models is facing efficiency and
privacy challenges. Recently, federated learning (FL) has emerged as an
alternative solution and continue to thrive in this new reality. Existing FL
protocol design has been shown to be vulnerable to adversaries within or
outside of the system, compromising data privacy and system robustness. Besides
training powerful global models, it is of paramount importance to design FL
systems that have privacy guarantees and are resistant to different types of
adversaries. In this paper, we conduct the first comprehensive survey on this
topic. Through a concise introduction to the concept of FL, and a unique
taxonomy covering: 1) threat models; 2) poisoning attacks and defenses against
robustness; 3) inference attacks and defenses against privacy, we provide an
accessible review of this important topic. We highlight the intuitions, key
techniques as well as fundamental assumptions adopted by various attacks and
defenses. Finally, we discuss promising future research directions towards
robust and privacy-preserving federated learning.Comment: arXiv admin note: text overlap with arXiv:2003.02133; text overlap
with arXiv:1911.11815 by other author
Preserving The Safety And Confidentiality Of Data Mining Information In Health Care: A literature review
Daily, massive volume of data are produced due to the internet of things'
rapid development, which has now permeated the healthcare industry. Recent
advances in data mining have spawned a new field of a study dubbed
privacy-preserving data mining (PPDM). PPDM technique or approach enables the
extraction of actionable insight from enormous volume of data while
safeguarding the privacy of individual information and benefiting the entire
society Medical research has taken a new course as a result of data mining with
healthcare data to detect diseases earlier and improve patient care. Data
integration necessitates the sharing of sensitive patient information. However,
substantial privacy issues are raised in connection with the storage and
transmission of potentially sensitive information. Disclosing sensitive
information infringes on patients' privacy. This paper aims to conduct a review
of related work on privacy-preserving mechanisms, data protection regulations,
and mitigating tactics. The review concluded that no single strategy
outperforms all others. Hence, future research should focus on adequate
techniques for privacy solutions in the age of massive medical data and the
standardization of evaluation standards
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