AI unmasks anonymous chess players, posing privacy risks

2022 ◽  
Keyword(s):  
Privacy Risks ◽  
Chess Players

Artificial intelligence unmasks anonymous chess players

Science ◽  
2022 ◽  
Vol 375 (6577) ◽  
pp. 129-129
Author(s):  
Matthew Hutson
Keyword(s):  
Artificial Intelligence ◽  
Privacy Risks ◽  
Chess Players

Software that identifies unique styles poses privacy risks

The perceptual differences between experienced Chinese chess players and novices: Evidence from eye movement

2016 ◽  
Vol 48 (5) ◽  
pp. 457
Author(s):  
Fuxing WANG ◽  
Xiujuan HOU ◽  
Zhaohui DUAN ◽  
Huashan LIU ◽  
Hui LI
Keyword(s):  
Eye Movement ◽  
Perceptual Differences ◽  
Chess Players

A market-based rationale for the privacy paradox

2021 ◽  
pp. 016344372110158
Author(s):  
Opeyemi Akanbi
Keyword(s):  
Social Media ◽  
Market Structures ◽  
Media Companies ◽  
The Social ◽  
Social Media Platforms ◽  
Privacy Paradox ◽  
The Individual ◽  
Privacy Risks ◽  
Social Collective

Moving beyond the current focus on the individual as the unit of analysis in the privacy paradox, this article examines the misalignment between privacy attitudes and online behaviors at the level of society as a collective. I draw on Facebook’s market performance to show how despite concerns about privacy, market structures drive user, advertiser and investor behaviors to continue to reward corporate owners of social media platforms. In this market-oriented analysis, I introduce the metaphor of elasticity to capture the responsiveness of demand for social media to the data (price) charged by social media companies. Overall, this article positions social media as inelastic, relative to privacy costs; highlights the role of the social collective in the privacy crises; and ultimately underscores the need for structural interventions in addressing privacy risks.

scholarly journals The Distribution of Privacy Risks: Who Needs Protection?

1998 ◽  
Vol 14 (4) ◽  
pp. 263-274 ◽  
Author(s):  
Charles D. Raab
Keyword(s):  
Privacy Risks

scholarly journals Privacy vs. Reward in Indoor Location-Based Services

2016 ◽  
Vol 2016 (4) ◽  
pp. 102-122 ◽  
Author(s):  
Kassem Fawaz ◽  
Kyu-Han Kim ◽  
Kang G. Shin
Keyword(s):  
Location Privacy ◽  
Indoor Localization ◽  
Service Providers ◽  
Location Based Services ◽  
Location Information ◽  
Privacy Concerns ◽  
Indoor Location ◽  
Location Data ◽  
Personalized Services ◽  
Privacy Risks

AbstractWith the advance of indoor localization technology, indoor location-based services (ILBS) are gaining popularity. They, however, accompany privacy concerns. ILBS providers track the users’ mobility to learn more about their behavior, and then provide them with improved and personalized services. Our survey of 200 individuals highlighted their concerns about this tracking for potential leakage of their personal/private traits, but also showed their willingness to accept reduced tracking for improved service. In this paper, we propose PR-LBS (Privacy vs. Reward for Location-Based Service), a system that addresses these seemingly conflicting requirements by balancing the users’ privacy concerns and the benefits of sharing location information in indoor location tracking environments. PR-LBS relies on a novel location-privacy criterion to quantify the privacy risks pertaining to sharing indoor location information. It also employs a repeated play model to ensure that the received service is proportionate to the privacy risk. We implement and evaluate PR-LBS extensively with various real-world user mobility traces. Results show that PR-LBS has low overhead, protects the users’ privacy, and makes a good tradeoff between the quality of service for the users and the utility of shared location data for service providers.

Online Social Networks: Why We Disclose

2010 ◽  
Vol 25 (2) ◽  
pp. 109-125 ◽  
Author(s):  
Hanna Krasnova ◽  
Sarah Spiekermann ◽  
Ksenia Koroleva ◽  
Thomas Hildebrand
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Information Disclosure ◽  
Structural Equation ◽  
Personal Information ◽  
Equation Model ◽  
Self Disclosure ◽  
Network Provider ◽  
Privacy Calculus ◽  
Privacy Risks

On online social networks such as Facebook, massive self-disclosure by users has attracted the attention of Industry players and policymakers worldwide. Despite the Impressive scope of this phenomenon, very little Is understood about what motivates users to disclose personal Information. Integrating focus group results Into a theoretical privacy calculus framework, we develop and empirically test a Structural Equation Model of self-disclosure with 259 subjects. We find that users are primarily motivated to disclose Information because of the convenience of maintaining and developing relationships and platform enjoyment. Countervailing these benefits, privacy risks represent a critical barrier to information disclosure. However, users’ perception of risk can be mitigated by their trust in the network provider and availability of control options. Based on these findings, we offer recommendations for network providers.

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