Privacy Is the Price: Player Views and Technical Evaluation of Data Practices in Online Games

被引：0

作者：

Bourdoucen A. ^{[1
]}

Nurgalieva L. ^{[1
]}

Lindqvist J. ^{[1
]}

机构：

[1] Aalto University, Espoo

来源：

Proceedings of the ACM on Human-Computer Interaction | 2023年 / 7卷 / CHI PLAY期

基金：

芬兰科学院;

关键词：

online games; privacy; security;

D O I：

10.1145/3611064

中图分类号：

学科分类号：

摘要：

Online games engage players in sharing their personal data with the games themselves and other players, which can pose security, privacy, and integrity risks to players. This paper presents an analysis of data practices in 21 online games and a qualitative interview study (N=20) that explores players' views on sharing their data in online games. Our results show that players' willingness to share personal information is contextual and related to game settings and game design elements. Our findings also highlight players' misconceptions and concerns surrounding data collection in games, and approaches to mitigate these concerns. Finally, this work identifies questionable design practices with online games and suggests design implications that will increase transparency and player control over data sharing. © 2023 Owner/Author.

引用

共 4 条

[1] The Price of Privacy An Evaluation of the Economic Value of Collecting Clickstream Data
Baumann, Annika
Haupt, Johannes
Gebert, Fabian
Lessmann, Stefan
BUSINESS & INFORMATION SYSTEMS ENGINEERING, 2019, 61 (04) : 413 - 431
[2] Privacy Practices of Health Social Networking Sites Implications for Privacy and Data Security in Online Cancer Communities
Charbonneau, Deborah H.
CIN-COMPUTERS INFORMATICS NURSING, 2016, 34 (08) : 355 - 359
[3] Privacy Rating: A User-Centered Approach for Visualizing Data Handling Practices of Online Services
Barth, Susanne
Ionita, Dan
De Jong, Menno D. T.
Hartel, Pieter H.
Junger, Marianne
IEEE TRANSACTIONS ON PROFESSIONAL COMMUNICATION, 2021, 64 (04) : 354 - 373
[4] Whales, Dolphins, or Minnows? Towards the Player Clustering in Free Online Games Based on Purchasing Behavior via Data Mining Technique
Yang, Wanshan
Yang, Gemeng
Huang, Ting
Chen, Lijun
Liu, Youjian
2018 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA), 2018, : 4101 - 4108

← 1 →