#Privacy pro for android android#
#Privacy pro for android windows#

However, misunderstandings of end-to-end encryption's benefits and shortcomings limit people's ability to make informed choices about how and when to use these services. Additionally, we highlight research gaps and we provide useful insights for future research, such as the need for a concrete definition (theme 1), the need for the proposal of privacy preventive technologies (theme 3 and 4) and the need for investigation of information privacy awareness in multiple contexts (theme 5).Īs large messaging providers increasingly adopt end-to-end encryption, private communication is readily available to more users than ever before. Based on the analysis of the literature, we identify five main attributes that constitute a “Profile for the Information Privacy Aware User”, stemming from the classification framework, and further, we suggest the way this profile can be beneficial for internet users, Internet providers and designers of privacy awareness enhancing technologies. We created a five-concept classification framework for the research topics that the academic community raises as important to be further investigated and the main challenges inhibiting information privacy awareness, and we classified all the selected papers according to this framework. This paper reports a systematic literature review of existing studies related to Internet users’ information privacy awareness, towards enhancing respective initiatives and defining the attributes that a user should have so as to be privacy aware. Despite the fact that in many cases internet users claim to be cognizant of privacy issues, they tend to jeopardize their privacy and take no actions to protection it. In today’s Internet reliant services, the issue of users’ information privacy awareness is being raised. Finally, the business aspects of performing the proposed statistical OS comparisons from the perspectives of OS developers, MNOs, device manufacturers, and end-users are highlighted.

#Privacy pro for android android#

Additionally, IOS has shown better performance than Android in large geographical areas of Turkey. On the other hand depending on the UE category, the performances of MNOs may vary when IOS and Android OSs are compared based on different KPIs. The outcome of the analysis indicate that Android devices perform better in terms of DL speed among all MNOs, whereas IOS devices are better in terms of latency values. The analysis results performed in this paper uses statistical comparisons of unpaired observations due to imbalance between number of observations between all OSs and yield insight on how the mobile OS types’ network performances differ using some important Key Performance Indicators (KPIs) such as downlink (DL) speed, latency, jitter and packet loss (PL).

#Privacy pro for android windows#

This paper’s focus is on performance comparisons of UE OSs (including Android, IOS (iPhone Operating System) and Windows phones) over different UE categories, MNOs and locations based on previously collected end-to-end nationwide crowd-sourced data measurements in Turkey. For this reason, fair and statistically accurate observed network performance differences of UE’s OSs based on category types, MNOs or locations can be of interest for mobile telecommunication ecosystem players. User equipment (UE)’s operating system (OS) and category types are important factors that are affecting the end-user performance in a given mobile network operator (MNO)’s infrastructure. These various techniques/methods are experimentalbased, features-based, social-impact based or image processing capability based evaluations. using OpenCV library functions) in, the effects of manipulating smartphone data on both Android and IOS platforms in, security/privacy feature comparisons in, mobile OS user differences in, review of mobile OS applications in, mobile app development comparisons of Android and IOS OSs in, the engagement levels of IOS and Android application developers in, application release practice comparisons of Android and IOS in, mobile OS platform features or ecology comparisons in when designing comparative methods between existing mobile OSs. Present works consider factors such as handover performances, connection establishment delays, network interface usage and selection capabilities in, the effect of mobility in, computer vision application performance (e.g. In the literature, no prior efforts have concentrated on comprehensive understanding of the statistical KPI performance differences of UE OSs based on UE categories and utilized MNOs using large-scale and nationwide collected real data measurements over a long time duration in Turkey.