Obfuscation maximization-based decision-making: Theory, methodology and first empirical evidence

Theories of decision-making are routinely based on the notion that decision-makers choose alternatives which align with their underlying preferences—and hence that their preferences can be inferred from their choices. In some situations, however, a decision-maker may wish to hide his or her preferences from an onlooker. This paper argues that...

Hiding opinions by minimizing disclosed information: an obfuscation-based opinion dynamics model

In the field of opinion dynamics, the hiding of opinions is routinely modeled as staying silent. However, staying silent is not always feasible. In situations where opinions are indirectly expressed by one’s observable actions, people may however try to hide their opinions via a more complex and intelligent strategy called obfuscation, which...

This Item Might Reinforce Your Opinion: Obfuscation and Labeling of Search Results to Mitigate Confirmation Bias

During online information search, users tend to select search results that confirm previous beliefs and ignore competing possibilities. This systematic pattern in human behavior is known as confirmation bias. In this paper, we study the effect of obfuscation (i.e., hiding the result unless the user clicks on it) with warning labels and the...

Towards user-oriented privacy for recommender system data: A personalization-based approach to gender obfuscation for user profiles

In this paper, we propose a new privacy solution for the data used to train a recommender system, i.e., the user–item matrix. The user–item matrix contains implicit information, which can be inferred using a classifier, leading to potential privacy violations. Our solution, called Personalized Blurring (PerBlur), is a simple, yet effective,...

Investigating the Perception and Effects of Misunderstandings in Java Code

Although writing code seems trivial at times, problems arise when humans misinterpret what source code actually does. One of the potential causes are “atoms of confusion”; the smallest possible patterns of misinterpretable source code. The misunderstandings and errors have been studied in past for the C programming language. They are found to...

PREDICT: Efficient Private Disease Susceptibility Testing in Direct-to-Consumer Model

Genome sequencing has rapidly advanced in the last decade, making it easier for anyone to obtain digital genomes at low costs from companies such as Helix, MyHeritage, and 23andMe. Companies now offer their services in a direct-to-consumer (DTC) model without the intervention of a medical institution. Thereby, providing people with direct...

Privacy-preserving consensus averaging

Consensus problem has been a topic of interest for many research areas allowing multiple agents to reach an agreement through local information exchange. The explicit share of the state variables, however, may cause privacy issues due to the confidentiality of the initial values. In this work, asynchronous privacy-preserving consensus average...

Protecting the grid topology and user consumption patterns during state estimation in smart grids based on data obfuscation

Smart grids promise a more reliable, efficient, economically viable, and environment-friendly electricity infrastructure for the future. State estimation in smart grids plays a pivotal role in system monitoring, reliable operation, automation, and grid stabilization. However, the power consumption data collected from the users during state...

BlUrM(or)e: Revisiting gender obfuscation in the user-item matrix

Past research has demonstrated that removing implicit gender information from the user-item matrix does not result in substantial performance losses. Such results point towards promising solutions for protecting users’ privacy without compromising prediction performance, which are of particular interest in multistakeholder environments. Here,...