Data Privacy, What Still Need Consideration in Online Application System?

This paper aims to conduct an analysis and exploration of matters that still needs to be considered in relation to data privacy in the online application system. This research is still a preliminary study. We conduct research related to data privacy using systematic literature review approach (SLR). Bt using SLR stages, we made a synthesis of 44 publications from Scopus Database Online that were released in the range 2015 - 2019. Based on this study, we found six things points to consider in data privacy, namely security and data protection, user awareness, risk managment, control setting, ethics, and transparency

GraphTrack: An unsupervised graph-based cross-device tracking framework

Cross-device tracking has drawn growing attention from both commercial companies and the general public because of its privacy implications as well as applications for user profiling, person- alized services, and user authentication. One particular, widely-used type of cross-device tracking is to leverage browsing histories of user devices, e.g., characterized by a list of IP addresses used by the devices and domains visited by the devices. State-of-the-art browsing history based methods compute a similarity score for a device pair using only the common IPs used by both devices and domains visited by both devices, and leverage supervised machine learning. These methods cannot capture latent correlations among IPs/domains and require a large amount of labeled device pairs, which is time-consuming and costly to obtain. In this work, GraphTrack, an unsupervised graph-based cross-device tracking framework, to track users across different devices by correlating browsing histories on these devices is proposed. Specifically, the complex interplays among IPs, domains, and devices are modeled as graphs to cap- ture the latent correlations between IPs/domains. Moreover, random walk with restart is adapted to compute similarity scores between devices based on the graphs. GraphTrack leverages the sim- ilarity scores to perform unsupervised cross-device tracking and can be extended to incorporate manual labels if available. GraphTrack is evaluated on a real-world dataset. The results show that GraphTrack substantially outperforms the state-of-the-art method, e.g., by 13% in Accuracy