Privacy and human behavior in the age of information

By Alessandro Acquisti, Laura Brandimarte, George Loewenstein. 2015

1,755 citations recorded by the source corpus.

Subfields: Communication, Sociology and Political Science

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Citations in the corpus

BLENDER: Enabling Local Search with a Hybrid Differential Privacy Model (2017, background): “The work most similar to ours, [34], performs evaluations using in the range [1, 10].”
(Un)informed Consent: Studying GDPR Consent Notices in the Field (2019, background): “This means that our findings are generally comparable, but specific results may differ based on website and category, which is what we would expect given that privacy preferences are highly contextual [3].”
‘Hypernudge’: Big Data as a mode of regulation by design (2017, background): “According to Acquisti and his colleagues, this extensive uncertainty and context dependence imply that people cannot be counted on to navigate the complex trade-offs involving privacy in a self-interested fashion (Acquisti et al., 2015).”
The price of anonymity: empirical evidence from a market for Bitcoin anonymization (2017, background): “So, what should the market price for anonymity be? While the economics of privacy [5] and personal data [6] have been studied for long, and empirical research has estimated price information (see [7] and [8] for reviews), remarkably little is known about the price of anonymity.”
How Short Is Too Short? Implications of Length and Framing on the Effectiveness of Privacy Notices (2016, background): “Here we discuss prior work on privacy notice design in three areas: short-form privacy notices, framing, and delivery methods.”
All One Needs to Know about Metaverse: A Complete Survey on Technological Singularity, Virtual Ecosystem, and Research Agenda (2021, background): “, personality) to create nudged [690] social interactions in the metaverse.”
Nudges for Privacy and Security (2017, background): “Behavioral economics and decision research have analyzed the hurdles individuals face when making privacy or security decisions online [Acquisti 2004; Acquisti et al. 2015].”
Machine Heuristic: When We Trust Computers More than Humans with Our Personal Information (2019, background): “Personal data are often tracked, shared, and even sold to third parties without the user’s explicit permission [1].”
When Machine Learning Meets Privacy (2020, background): “The authors of [3] indicate that users continuously modify their privacy requirements to reach their expected level of privacy, and also, appropriately change their privacy preferences.”
Secrets and Likes: The Drive for Privacy and the Difficulty of Achieving It in the Digital Age (2020, background): “These factors range from privacy “calculus” to emotions; from asymmetric information to bounded rationality; and from resignation and learned helplessness to cognitive and behavioral biases (Acquisti et al., 2015).”
Engineering Information Disclosure: Norm Shaping Designs (2016, background): “In particular, this literature posits that the disparity between professed attitudes and behavior stems from either uncertainty due to incomplete information about one’s preferences or from uncertainty about which social rules apply in that context [2].”
Assisting Users in a World Full of Cameras: A Privacy-Aware Infrastructure for Computer Vision Applications (2017, background): “people often do not know their own privacy preferences for a particular context, and the specific harms that may arise are not always tangible [24].”