Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

Konrad Blaszkiewicz

dblp:185/3956 · DBLP profile ↗
← Back
2ranked-venue papers
0as first author
0since 2021 · last 2017
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Human-computer interaction and ubiquitous computing · 2

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Network and information security
1 paper
Privacy and data protection · 100%
Human-computer interaction and pervasive computing
1 paper
Ubiquitous computing and smart environments · 100%

Topics — the 2 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Ubiquitous computing and smart environments › mobile computing
mobile app usage analysis
0.112016
Differentiating smartphone users by app usage · UbiComp 2016
Ubiquitous computing and smart environments
mobile computing
0.112016
Differentiating smartphone users by app usage · UbiComp 2016

Methods — techniques the papers use, named apart from their topics

hamming distance analysis · 0.5behavioral fingerprinting · 0.5
YearPublicationVenuePosition
2017 Impact of location-based games on phone usage and movement: a case study on Pokémon GO
abstract
Pokémon GO was a short lived mobile location-based gaming phenomenon. After its launch in July 2016, it quickly reached 500 million installs, but afterwards interest faded. As part of a large scale "in the wild" mobile phone study we have recorded phone usage and location measurements between June and September 2016. We investigate who were the people who installed and played Pokémon GO and what effects it had on their behaviour. We chose as a middle point the start of playing the game, and selected users that had activity for at least two weeks before and two weeks after it. In this work we present our findings on a sample of 2, 861 users. We compare demographic characteristics and Big Five personality traits of these users with 7, 904 non-playing users from the same time period. The general daily phone usage of players increased on average by 27 minutes, which represents 16% per day. In terms of large scale movement patterns, these did not change, with regard to diameter and total path length per day.
Ionut Andone, Konrad Blaszkiewicz, Matthias Böhmer 0001, Alexander Markowetz
MobileHCI2
2016 Differentiating smartphone users by app usage
abstract
Tracking users across websites and apps is as desirable to the marketing industry as it is unalluring to users. The central challenge lies in identifying users from the perspective of different apps/sites. While there are methods to identify users via technical settings of their phones, these are prone to countermeasures. Yet, in this paper, we show that it is possible to differentiate users via their set of used apps, their app signature. To this end, we investigate the app usage of 46726 participants from the Menthal project. Even limiting our observation to the 500 globally most frequent apps results in unique signatures for 99.67% of users. Furthermore, even under this restriction, the average minimum Hamming distance to the closest other user is 25.93. Avoiding identification would thus require a massive change in the behavior of a user. Indeed, 99.4% of all users have unique usage patterns among the top 60 globally used apps. In contrast to previous work, this paper differentiates between users based on behavior instead of technical parameters. It thus opens an entirely new discussion regarding privacy.
Pascal Welke, Ionut Andone, Konrad Blaszkiewicz, Alexander Markowetz
UbiComp3