Who Am I?

To enable targeted ads, companies profile Internet users, automatically inferring potential interests and demographics. While current profiling centers on users' web browsing data, smartphones and other devices with rich sensing capabilities portend profiling techniques that draw on methods from ubiquitous computing. Unfortunately, even existing profiling and ad-targeting practices remain opaque to users, engendering distrust, resignation, and privacy concerns. We hypothesized that making profiling visible at the time and place it occurs might help users better understand and engage with automatically constructed profiles. To this end, we built a technology probe that surfaces the incremental construction of user profiles from both web browsing and activities in the physical world. The probe explores transparency and control of profile construction in real time. We conducted a two-week field deployment of this probe with 25 participants. We found that increasing the visibility of profiling helped participants anticipate how certain actions can trigger specific ads. Participants' desired engagement with their profile differed in part based on their overall attitudes toward ads. Furthermore, participants expected algorithms would automatically determine when an inference was inaccurate, no longer relevant, or off-limits. Current techniques typically do not do this. Overall, our findings suggest that leveraging opportunistic moments within pervasive computing to engage users with their own inferred profiles can create more trustworthy and positive experiences with targeted ads.

Web-Browsing Application Using Web Scraping Technology in Korean Network Separation Application

Attackers’ intrusion into the Enterprise LAN is increasing every year, and the method is becoming more intelligent and crafty. Various security measures against external network intrusions, such as firewalls, are being studied and applied to protect against external attacks, but it is difficult to respond to increasing attacks. Most institutions block access from the external network for the safety of the internal network and allow access from the internal network to the external network through some restricted ports. In particular, restricted ports in subject to a variety of security techniques to block intrusion into the internal network, but in the process, access to the internal network is only applied by restricted ports, making it inefficient to handle internal requests. Although various studies have been conducted on network isolation to address these challenges, it is difficult to perform tasks efficiently as security functions, such as detecting whether request data is attacked or not, during actual application. The proposed technique is a network-blocking-based network separation technique that converts data from the external network connected to the Internet into symmetry data from which malicious code is removed through an agent and delivers it to the client of the internal network. We propose a technique to provide access.

Differentially Private Web Browsing Trajectory over Infinite Streams

Nowadays, a lot of data mining applications, such as web traffic analysis and content popularity prediction, leverage users’ web browsing trajectories to improve their performance. However, the disclosure of web browsing trajectory is the most prominent issue. A novel privacy model, named Differential Privacy, is used to rigorously protect user’s privacy. Some works have applied this privacy model to spatial-temporal streams. However, these works either protect the users’ activities in different places separately or protect their activities in all places jointly. The former one cannot protect trajectories that traverse multiple places; while the latter ignores the differences among places and suffers the degradation of data utility (i.e., data accuracy). In this paper, we propose a w , n -differential privacy to protect any spatial-temporal sequence occurring in w successive timestamps and n -range places. To achieve better data utility, we propose two implementation algorithms, named Spatial-Temporal Budget Distribution (STBD) and Spatial-Temporal RescueDP (STR). Theoretical analysis and experimental results show that these two algorithms can achieve a balance between data utility and trajectory privacy guarantee.

Perancangan Aplikasi Galeri E-Jurnal Dengan REST API Berbasis Android

<p><em>Sistem pencarian jurnal masih dilakukan secara bertahap dengan menggunakan web browsing. Sistem pencarian bertahap ini memiliki beberapa kelemahan, antara lain proses pencarian jurnal yang lambat dan kurang efisien. Dari permasalahan diatas maka penulis mengusulkan untuk merancang dan membangun suatu sistem untuk melaksanakan pencarian jurnal dengan memanfaatkan teknologi informasi yang disebut aplikasi jurnal elektronik atau istilah sekarang bernama e-journal menggunakan rest api diaplikasikan melalui smartphone android yang dirancang dengan sangat sederhana dan biaya yang relatif murah sehingga memudahkan para pengguna. Penggunaan teknologi smartphone android saat ini banyak dipakai untuk ketersediaan transaksi elektronik, karena kecepatan, keamanan dan ketepatan data yang dihasilkan.Aplikasi jurnal elektronik menggunakan teknologi berbasis android</em> <em>dirancang dengan sangat sederhana sehingga memudahkan para pengguna agar dapat dijadikan sebagai media alternatif untuk pencarian jurnal secara elektronik.</em></p>

Online News in India: Appraising the Digital News Consumption Landscape in theWorld’s Largest Democracy (2014-2018)

How do people in the world's largest democracy consume online news? This article reports findings from the analysis of a novel empirical dataset tracking the web-browsing behavior of more than 50,000 Indian internet users over 45 months. In doing so, it seeks to understand the digital news consumption landscape of a crucial, but understudied context and appraise the prominence and longitudinal trends of the audience share of different types of news sources in the online Indian space. It finds that while digital-born media have not contested the hegemony of legacy media, regional vernacular media have suffered significant declines in their audience shares. The article proposes the concept of audience mobility, using it to identify qualitatively distinct dynamics in how vernacular audiences in India have migrated to national vis-à-vis international outlets. The findings are discussed in light of contemporary changes in Indian society that is characterized by increasing digitization and literacy.

Personalized Event Recommendation System

Many times, we receive a large number of notifications about various events, exhibitions, and meetups happening all around us that are irrelevant to us because we simply are not interested in them. Various people have their importance of things that they are interested in and be notified of all these events and from them searching for something that might interest them will take a lot of time and sometimes does not provide any meaningful information. In today’s world, there is no such existing facility that notifies us about the various events that are tailored to our interest strictly based on our web browsing history. Thus, we aim to create a Personalized Event Recommendation System that recommends the events that are sorted according to the user based on his/her interests using their browser history.

Towards Lightweight URL-Based Phishing Detection

Nowadays, the majority of everyday computing devices, irrespective of their size and operating system, allow access to information and online services through web browsers. However, the pervasiveness of web browsing in our daily life does not come without security risks. This widespread practice of web browsing in combination with web users’ low situational awareness against cyber attacks, exposes them to a variety of threats, such as phishing, malware and profiling. Phishing attacks can compromise a target, individual or enterprise, through social interaction alone. Moreover, in the current threat landscape phishing attacks typically serve as an attack vector or initial step in a more complex campaign. To make matters worse, past work has demonstrated the inability of denylists, which are the default phishing countermeasure, to protect users from the dynamic nature of phishing URLs. In this context, our work uses supervised machine learning to block phishing attacks, based on a novel combination of features that are extracted solely from the URL. We evaluate our performance over time with a dataset which consists of active phishing attacks and compare it with Google Safe Browsing (GSB), i.e., the default security control in most popular web browsers. We find that our work outperforms GSB in all of our experiments, as well as performs well even against phishing URLs which are active one year after our model’s training.