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      Behavioral authentication for security and safety

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          Abstract

          The issues of both system security and safety can be dissected integrally from the perspective of behavioral appropriateness. That is, a system that is secure or safe can be judged by whether the behavior of certain agent(s) is appropriate or not. Specifically, a so-called appropriate behavior involves the right agent performing the right actions at the right time under certain conditions. Then, according to different levels of appropriateness and degrees of custodies, behavioral authentication can be graded into three levels, i.e., the authentication of behavioral Identity, Conformity, and Benignity. In a broad sense, for the security and safety issue, behavioral authentication is not only an innovative and promising method due to its inherent advantages but also a critical and fundamental problem due to the ubiquity of behavior generation and the necessity of behavior regulation in any system. By this classification, this review provides a comprehensive examination of the background and preliminaries of behavioral authentication. It further summarizes existing research based on their respective focus areas and characteristics. The challenges confronted by current behavioral authentication methods are analyzed, and potential research directions are discussed to promote the diversified and integrated development of behavioral authentication.

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          Membership Inference Attacks Against Machine Learning Models

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            Private traits and attributes are predictable from digital records of human behavior.

            We show that easily accessible digital records of behavior, Facebook Likes, can be used to automatically and accurately predict a range of highly sensitive personal attributes including: sexual orientation, ethnicity, religious and political views, personality traits, intelligence, happiness, use of addictive substances, parental separation, age, and gender. The analysis presented is based on a dataset of over 58,000 volunteers who provided their Facebook Likes, detailed demographic profiles, and the results of several psychometric tests. The proposed model uses dimensionality reduction for preprocessing the Likes data, which are then entered into logistic/linear regression to predict individual psychodemographic profiles from Likes. The model correctly discriminates between homosexual and heterosexual men in 88% of cases, African Americans and Caucasian Americans in 95% of cases, and between Democrat and Republican in 85% of cases. For the personality trait "Openness," prediction accuracy is close to the test-retest accuracy of a standard personality test. We give examples of associations between attributes and Likes and discuss implications for online personalization and privacy.
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              Limits of predictability in human mobility.

              A range of applications, from predicting the spread of human and electronic viruses to city planning and resource management in mobile communications, depend on our ability to foresee the whereabouts and mobility of individuals, raising a fundamental question: To what degree is human behavior predictable? Here we explore the limits of predictability in human dynamics by studying the mobility patterns of anonymized mobile phone users. By measuring the entropy of each individual's trajectory, we find a 93% potential predictability in user mobility across the whole user base. Despite the significant differences in the travel patterns, we find a remarkable lack of variability in predictability, which is largely independent of the distance users cover on a regular basis.
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                Author and article information

                Contributors
                Journal
                sands
                https://sands.edpsciences.org
                Security and Safety
                Security and Safety
                EDP Sciences and CSPM
                2826-1275
                07 May 2024
                2024
                30 April 2024
                30 April 2024
                : 3
                : ( publisher-idID: sands/2024/01 )
                : 2024003
                Affiliations
                [1 ] Department of Computer Science and Technology, Tongji University, , Shanghai, 201804, China,
                [2 ] Key Laboratory of Embedded System and Service Computing, Ministry of Education, , Shanghai, 201804, China,
                [3 ] Shanghai Artificial Intelligence Laboratory, , Shanghai, 200232, China,
                Author notes
                [* ]Corresponding authors (email: cwang@ 123456tongji.edu.cn (Cheng Wang); cjjiang@ 123456tongji.edu.cn (Changjun Jiang))
                Article
                sands20230028
                10.1051/sands/2024003
                7edf9efd-667a-40e0-b5f2-a5035aa4ac2c
                © The Author(s) 2024. Published by EDP Sciences and China Science Publishing & Media Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 07 December 2023
                : 05 March 2024
                : 18 March 2024
                Page count
                Figures: 11, Tables: 3, Equations: 3, References: 191, Pages: 36
                Categories
                Review
                Digital Finance
                Custom metadata
                Security and Safety, Vol. 3, 2024003 (2024)
                2024
                2024
                2024
                yes

                behavioral authentication,artificial intelligence,machine learning,anomaly detection,behavior modeling,security and safety

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