Results 11 to 20 of about 11,259,019 (289)

Information privacy concern at individual, group, organization and societal level - a literature review [PDF]

open access: yesVilakshan (XIMB Journal of Management), 2021
Purpose – In today’s digitized environment, information privacy has become a prime concern for everybody. The purpose of this paper is to provide an understanding of information privacy concern arising because of the application of computer-based ...
Dillip Kumar Rath, Ajit Kumar
doaj   +2 more sources

Privacy-Utility Management of Hypothesis Tests [PDF]

open access: yesarXiv, 2018
The trade-off of hypothesis tests on the correlated privacy hypothesis and utility hypothesis is studied. The error exponent of the Bayesian composite hypothesis test on the privacy or utility hypothesis can be characterized by the corresponding minimal ...
Li, Zuxing, Oechtering, Tobias J.
core   +3 more sources

Privacy and Health Information Technology [PDF]

open access: green, 2009
The increased use of health information technology (health IT) is a common element of nearly every health reform proposal because it has the potential to decrease costs, improve health outcomes, coordinate care, and improve public health.
Deven McGraw   +3 more
core   +4 more sources

How Perceptions of Information Privacy and Security Impact Consumer Trust in Crypto-Payment: An Empirical Study

open access: yesIEEE Access, 2022
The ever-increasing acceptance of cryptocurrencies has fueled applications beyond investment purposes. Crypto-payment is one such application that can bring radical changes to financial transactions in many industries, particularly e-commerce and online ...
Atefeh Mashatan   +2 more
semanticscholar   +1 more source

Validity and Reliability of the Scale Internet Users’ Information Privacy Concerns (IUIPC)

open access: yesProceedings on Privacy Enhancing Technologies, 2021
Internet Users’ Information Privacy Concerns (IUIPC-10) is one of the most endorsed privacy concern scales. It is widely used in the evaluation of human factors of PETs and the investigation of the privacy paradox. Even though its predecessor Concern For
Thomas Gross
semanticscholar   +1 more source

Automatically Attributing Mobile Threat Actors by Vectorized ATT&CK Matrix and Paired Indicator

open access: yesSensors, 2021
During the past decade, mobile attacks have been established as an indispensable attack vector adopted by Advanced Persistent Threat (APT) groups. The ubiquitous nature of the smartphone has allowed users to use mobile payments and store private or ...
Kyoungmin Kim   +3 more
doaj   +1 more source

Joint Protection of Energy Security and Information Privacy for Energy Harvesting: An Incentive Federated Learning Approach

open access: yesIEEE Transactions on Industrial Informatics, 2021
Energy harvesting (EH) is a promising and critical technology to mitigate the dilemma between the limited battery capacity and the increasing energy consumption in the Internet of everything. However, the current EH system suffers from energy-information
Qianqian Pan   +5 more
semanticscholar   +1 more source

Checking Only When It Is Necessary: Enabling Integrity Auditing Based on the Keyword With Sensitive Information Privacy for Encrypted Cloud Data

open access: yesIEEE Transactions on Dependable and Secure Computing, 2021
The public cloud data integrity auditing technique is used to check the integrity of cloud data through the Third Party Auditor (TPA). In order to make it more practical, we propose a new paradigm called integrity auditing based on the keyword with ...
Xiang Gao   +4 more
semanticscholar   +1 more source

Context-Aware Local Information Privacy

open access: yesIEEE Transactions on Information Forensics and Security, 2021
In this paper, we study Local Information Privacy (LIP). As a context-aware privacy notion, LIP relaxes the de facto standard privacy notion of local differential privacy (LDP) by incorporating prior knowledge and therefore achieving better utility.
Bo Jiang   +3 more
semanticscholar   +1 more source

Deep Learning with Differential Privacy [PDF]

open access: yesConference on Computer and Communications Security, 2016
Machine learning techniques based on neural networks are achieving remarkable results in a wide variety of domains. Often, the training of models requires large, representative datasets, which may be crowdsourced and contain sensitive information.
Martín Abadi   +6 more
semanticscholar   +1 more source

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