User Identification Framework Research Articles

Design of Automatic User Identification Framework in Crowdsourcing Requirements Engineering : User Mapping and System Architecture

The requirement elicitation is the initial stage of requirement engineering where information collected from users. The process are significantly determined by the quality and quantity of information collected. The crowdsourcing is a method of information gathering from many users. The number and variety of users in the crowdsourcing are both advantages and challenges in the elicitation process. This study purposes a framework for user identification that consists of user mapping and architecture system. The identification process consists of 8 main states, start with defining context, user target and scope determination, data source determination, user data collection, data pre-processing, feature selection, data classification and user identification. The results of this study is an initial state for development of an automated tool for user identification to elicit requirement through crowdsourcing. By the framework can be generated the user classification, which can be used to apply the appropriate method for gathering information in elicitation process.

A Unified Framework for User Identification Across Online and Offline Data

User identification across multiple datasets has a wide range of applications and there has been an increasing set of research works on this topic during recent years. However, most of existing works focus on user identification with a single input data type, e.g., (I) identifying a user across multiple social networks with online data and (II) detecting a single user from heterogeneous trajectory datasets with offline data. Different from previous works, in this paper, we propose a framework on user identification between online and offline datasets. We build connections between these two types of data by a mapping from IP addresses to physical locations. To solve this problem, we propose a novel framework consisting of three steps. First, we use a clustering method based on locations of IP addresses to map IP addresses into specific physical location distributions. Second, we propose a novel pairwise index to reduce space cost and running time for computing the co-occurrence. Lastly, we apply a learning-to-rank method to merge the effect of multiple features we get in the first two steps. Based on our framework, we design experiments to demonstrate the efficiency (in time and space) of our framework, together with the precision and recall of our approach compared to other methods.

User Identification Based on Integrating Multiple User Information across Online Social Networks

User identification can help us build more comprehensive user information. It has been attracting much attention from academia. Most of the existing works are profile-based user identification and relationship-based user identification. Due to user privacy settings and social network restrictions on user data crawl, user data may be missing or incomplete in real social networks. User data include profiles, user-generated contents (UGCs), and relationships. The features extracted in previous research may be sparse. In order to reduce the impact of the above problems on user identification, we propose a multiple user information user identification framework (MUIUI). Firstly, we develop multiprocess crawlers to obtain the user data from two popular social networks, Twitter and Facebook. Secondly, we use named entity recognition and entity linking to obtain and integrate locations and organizations from profiles and UGCs. We also extract URLs from profiles and UGCs. We apply the locations jointly with the relationships and develop several algorithms to measure the similarity of the display name, all locations, all organizations, location in profile, all URLs, following organizations, and user ID, respectively. Afterward, we propose a fusion classifier machine learning-based user identification method. The results show that the F1 score of MUIUI reaches 86.46% on the dataset. It proves that MUIUI can reduce the impact of user data that are missing or incomplete.

Deep Neural Network-based Relationship Identification Framework to Discriminate Fake Profile Over Social Media

Involvement of social media like personal, business and political propaganda activities, attracts anti-social activities and has also increased. Anti-social elements get a wider platform to spread negativity after hiding their identity behind fake and false profiles. In this paper, an analytical and methodological user identification framework is developed to significantly binds implicit and explicit link relationship over the end-users graphical perspective. Identify malicious user, its communal information and sockpuppet node. Apart from that, this work provides the concept of the deep neural network approach over the graphical and linguistic perspective of end-user to classify as malicious, fake and genuine. This concept also helps identify the trade-off between the similarity of nodes attributes and the density of connections to classifying identical profile as sockpuppet over social media.

Mobile Social Service User Identification Framework Based on Action-Characteristic Data Retention

Mobile social services are an indispensable part of our daily lives. These services are also favored by criminals because it is difficult to retrieve communication data from them. In the past, communication data provided by telecommunication carriers usually indicated when, from where, and with whom the communication occurred. Presently, it is difficult for law enforcement agencies and public security departments to obtain information regarding mobile social services. For this reason, these departments have requested Internet access service providers to store data that can be used to identify the user of a mobile social service at any given time. However, many non-government and civil society organizations claim that these practices violate privacy rights; hence, they strongly oppose the retention of the subscribers’ data by the government. Currently, the European Union law does not allow “general and indiscriminate retention of traffic data and location data,” except for “targeted” use against “serious crimes.” Under this premise, ensuring the necessary data retention, while reducing the privacy violations and maintaining public security is a challenging task. In this study, a novel identification framework based on different types and action characteristics of mobile social services is proposed. Based on this framework, government agencies do not need to retain general and indiscriminate traffic data, but only data that aid in identification. Thus, this framework substantially reduces the volume of potential targets and improves the probability of correct target identification, ensuring a balance between privacy and public security.

A Novel framework of EEG-based user identification by analyzing music-listening behavior

This paper introduces a novel framework for user identification by analyzing neuro-signals. Studies regarding Electroencephalography (EEG) revealed that such bio-signals are sensitive, hard to forge, confidential, and unique which the conventional biometric systems like face, speaker, signature and voice lack. Traditionally, researchers investigated the neuro-signal patterns by asking users to perform various imaginary, visual or calculative tasks. In this work, we have analyzed this neuro-signal pattern using audio as stimuli. The EEG signals are recorded simultaneously while user is listening to music. Four different genres of music are considered as users have their own preference and accordingly they respond with different emotions and interests. The users are also asked to provide music preference which acts as a personal identification mechanism. The framework offers the benefit of uniqueness in neuro-signal pattern even with the same music preference by different users. We used two different classifiers i.e. Hidden Markov Model (HMM) based temporal classifier and Support Vector Machine (SVM) for user identification system. A dataset of 2400 EEG signals while listening to music was collected from 60 users. User identification performance of 97.50 % and 93.83 % have been recorded with HMM and SVM classifiers, respectively. Finally, the performance of the system is also evaluated on various emotional states after showing different emotional videos to users.

Fast Lead User Identification Framework

Large portion of product innovation and development is accomplished by customers and only a small segment of the customer population engages in such innovation activities. Empirical research has shown that users in this subgroup, called lead users, tend to experience needs before the rest of the marketplace and stand to benefit greatly by finding solutions to those needs. To meet the challenge of quickly and effectively identifying lead users and uncovering their innovation ideas, the authors propose a fast and systematic approach, called Fast Lead User IDentification (FLUID), utilizing data mining techniques to identify lead users on social networking sites. The paper describes the steps taken to build and optimize the FLUID system to effectively identify lead users on the micro-blogging site Twitter. This entails studies using validated lead user questionnaires resulting in clusters of lead and non-lead Twitter users for a single product. The gathered online user metadata and behavior are then used as training data for the automated system. An overview of data processing techniques and relations to the empirically derived lead user characteristics are presented. Finally, classification algorithms that help to separate lead users from non-lead users are discussed, including optimization leading to the validation of the proposed approach. By making use of data-mining techniques on data rich sites like social networking sites, the FLUID approach minimizes the resource and time costs in identifying lead users and this provides a step towards systematizing the fuzzy-front end of the new product development process.

User Identification Framework in Social Network Services Environment

Social Network Service is a one of the service where people may communicate with one another; and may also exchange messages even of any type of audio or video communication. Social Network Service as name suggests a type of network. Such type of web application plays a dominant role in internet technology. In such type of online community, people may share their common interest. Facebook LinkedIn, orkut and many more are the Social Network Service and it is good medium of making link with people having unique or common interest and goals. But the problem of privacy protection is a big issue in today's world. As social networking sites allows anonymous users to share information of other stuffs. Due to which cybercrime is also increasing to a rapid extent. In this article we preprocessed the web log data of Social Network Services and assemble that data on the basis of image file format like jpg, jpeg, gif, png, bmp etc. and also propose a framework for victim's identification.Keywords: Preprocessing, Web Log Data, Social Network Service, Data Mining, Cyber Crime1 IntroductionNow days we can see social networking services are growing day by day, First of all the term of social network is a social structure made of individuals (or organizations) called nodes, which are tied (connected) by one or more specific types of interdependency, such as friendship, kinship, financial exchange, dislike or relationships of beliefs, knowledge or prestige [8].Social networking is the grouping of individuals into specific groups, like small rural communities or a neighbourhood subdivision, if you will [3]. Generally some social services focus on the privacy issue too. Due to which one person could not see the friend link of other one. There are various types of social network are available. Personal networks-These kinds of network allow creating profiles online and linked with other person, with a focus on social linkage such as friendship. For example, Orkut, LinkedIn, Facebook, MySpace are stage for communicating with contacts [9][4][13]. These kinds' networks involve users exchanging information with people. Statuses update networks-These types of social networks are developed to allow small status update to person in order to communicate with other persons. For example, Twitter, these types of network people may exchange messages or thoughts day by day. Location networks-These networks are developed to show one's real-time location, either as public information or as an update viewable to authorized contacts. Content sharing networks-These networks are designed as platforms for sharing content, such as music, photographs and videos. For example YouTube and Flicker. Shared interest networks-Some social networks are built around a common interest or geared to a specific group of people. These networks incorporate features from other types of social networks but are slanted toward a subset of individuals, such as those with similar hobbies, educational backgrounds, political affiliations, ethnic backgrounds, religious views, sexual orientations or other defining interests. For example LinkedIn [16].Figure 1 shows that the social relationships among different users. In social networking services any person can interacts another person we have shown the person using dot in the give Fig. and maintained its relationships through lines. In this network any number of users (u1, u2, u3 ...) can communicate each other.But their exists some services that do not suffer from such kind of issues like LinkedIn Facebook, orkut etc. 'Mutual friend' is that feature existing on such services which provides a way of connecting through other person.Mutual friends are the persons who are Social Networking Service (SNS) friends with both you and the person whose timeline you are viewing. Or we can say mutual friends are the common friends of a person. Suppose u1 is friend of u10 and u14 then u1 is mutual friend of u10, u14. According to Fig. …