Gopal Ram D, Hidaji Hooman, Patterson Raymond A, Yaraghi Niam
Information Systems and Management, Warwick Business School, University of Warwick, Coventry, UK.
Business Technology Management, Haskayne School of Business, University of Calgary, Calgary, Canada.
JAMIA Open. 2021 Nov 9;4(4):ooab100. doi: 10.1093/jamiaopen/ooab100. eCollection 2021 Oct.
To examine the impact of coronavirus disease 2019 (COVID-19) pandemic on the extent of potential violations of Internet users' privacy.
We conducted a longitudinal study of the data sharing practices of the top 1000 websites in the United States between April 9 and August 27, 2020. We fitted a conditional latent growth curve model on the data to examine the longitudinal trajectory of the third-party data sharing over the 21 weeks period of the study and examine how website characteristics affect this trajectory. We denote websites that asked for permission before placing cookies on users' browsers as "privacy-respecting."
As the weekly number of COVID-19 deaths increased by 1000, the average number of third parties increased by 0.26 (95% confidence interval [CI] 0.15-0.37) < 0.001 units in the next week. This effect was more pronounced for websites with higher traffic as they increased their third parties by an additional 0.41 (95% CI 0.18-0.64); < 0.001 units per week. However, privacy respecting websites that experienced a surge in traffic reduced their third parties by 1.01 (95% CI -2.01 to 0); = 0.05 units per week in response to every 1000 COVID-19 deaths in the preceding week.
While in general websites shared their users' data with more third parties as COVID-19 progressed in the United States, websites' expected traffic and respect for users' privacy significantly affect such trajectory.
Attention should also be paid to the impact of the pandemic on elevating online privacy threats, and the variation in third-party tracking among different types of websites.
研究2019年冠状病毒病(COVID-19)大流行对互联网用户隐私潜在侵犯程度的影响。
我们对2020年4月9日至8月27日期间美国排名前1000的网站的数据共享行为进行了纵向研究。我们对数据拟合了条件潜在增长曲线模型,以研究在21周的研究期间第三方数据共享的纵向轨迹,并考察网站特征如何影响这一轨迹。我们将在用户浏览器上放置cookies之前请求许可的网站称为“尊重隐私的网站”。
随着每周COVID-19死亡人数增加1000人,下一周第三方的平均数量增加0.26(95%置信区间[CI]0.15 - 0.37)<0.001个单位。对于流量较高的网站,这种影响更为明显,因为它们的第三方数量每周额外增加0.41(95%CI 0.18 - 0.64);<0.001个单位。然而,流量激增的尊重隐私的网站,针对前一周每1000例COVID-19死亡病例,其第三方数量每周减少1.01(95%CI -2.01至0);=0.05个单位。
在美国,随着COVID-19疫情的发展,一般来说网站会与更多第三方共享用户数据,但网站的预期流量和对用户隐私的尊重会显著影响这一轨迹。
还应关注大流行对加剧在线隐私威胁的影响,以及不同类型网站之间第三方跟踪的差异。