User's Personal Information Research Articles

URL Based Phishing Detection

Damage caused due to phishing attacks is that which targets the user's personal information. Phishing includes sending a user an email, or causing a phishing page to steal personal information from a user. Blacklist-based detection techniques can detect this form of attack; however, these approaches have certain limitations, and the number of people affecting have continued to grow. The aim of a technique for phishing detection using machine learning to identify each URL into either a legitimate URL or a phished URL. Data availability here in this proposed solution is the key to executing the solution and if there is any issue with data availability it can cost the project accuracy. Data used for model testing must be reliable and appropriate to almost identify all the websites that the user wants to check. Model consistency is another factor that may trigger project failure so the model has to be accurate in determining a true identity of URLs. This technique employs features of a standardized resource locator (URL). The features have been defined which contain URLs for the phishing site. The suggested approach employs certain characteristics to detect phishing. The strategy was tested with a data collection of 3,000 URLs for the phishing site and 3,000 valid URLs for the site. The findings show that more than 90 percent of phishing sites can be identified by the proposed technique.

A Survey on User’s Location Detail Privacy-Preserving Models

Protecting personal information of users is a challenging task in the area of computer network security in the recent days. Personal information of users is highly essential to provide location-based services (LBS), and at the same, it is important to protect it from unauthorized users. Users cannot avoid the sharing of location information in the current era of social networks. In order to provide continuous LBS, personal data of the users are required. When users want to use social networks, they worry about preserving their privacy. Users normally prefer to maintain their location secure and safe in social networks for many privacy reasons. Security cracks in social network lead to tracking of user location, and they may misuse user’s data. In this paper, we present different techniques that are used to protect location privacy of the users. Also it helps us to select an appropriate technique to protect location information of the user.

A Practical System for Privacy-Aware Targeted Mobile Advertising Services

With the prosperity of mobile application markets, mobile advertising is becoming an increasingly important economic force. In order to maximize revenue, ads are recommended to be delivered to potentially interested users, which requires user targeting, i.e., analyzing users’ profiles and exploring users’ interests. However, collecting user personal information for targeted mobile advertising services raises critical privacy concerns. Although some solutions like anonymization and obfuscation have been proposed for privacy-aware targeted advertising, they undesirably face the issues of security, efficiency, and/or ad relevance. In this paper, we propose a practical system enabling secure and efficient targeted mobile advertising services. It allows the ad network to perform accurate user targeting, while ensuring strong privacy protection for mobile users. Specifically, we show how to properly leverage a cryptographic primitive called private stream searching to support secure, accurate, and practical targeted mobile ad delivery. Moreover, we propose secure billing schemes to enable the ad network to charge advertisers in a privacy-preserving manner. The security strength of our system is thoroughly analyzed. Through extensive experiments, we show that our system achieves practical efficiency on mobile devices.

Pishing Attacks in Network Security

In the last few decays, phishing tricks have swiftly grown posing enormous threat to worldwide Internet security. These days, phishing attacks are one of the utmost common and serious threats over internet whereas cyber attackers are trying to steal users personal information regarding their financial assets by using different malwares and social engineering. The usual way of phishing attacks use some electronic messaging like emails or by providing the links that appears to be legitimate sites but actually these sites are malicious and controlled by the attackers. To detect phishing attack at high accuracy is always a crucial and has been great issue of interest. Recently many detection techniques has been introduced which are specifically designed for the detection of phishing with extreme accuracy. In this report the phishing attacks are discuss with some of the techniques which are proposed in various literature.

Nonnegative matrix factorization‐based privacy‐preserving collaborative filtering on cloud computing

AbstractThe security of user personal information on cloud computing is an important issue for the recommendation system. In order to provide high‐quality recommendation services, privacy of user is often obtained by untrusted recommendation systems. At the same time, malicious attacks often use the recommendation results to try to guess the private data of user. This article proposes a hybrid algorithm based on nonnegative matrix factorization and random perturbation technology, which implements the recommendation system and solves the protection problem of user privacy data in the recommendation process on cloud computing. Compared with the privacy protection algorithm of SVD, the elements of the matrix after the decomposition of the new algorithm are nonnegative elements, avoiding the meaninglessness of negative numbers in the matrix formed by texts, images, and so on, and it has a good explanation for the local characteristics of things. Experiments show that the new algorithm can produce recommendation results with certain accuracy under the premise of protecting users' personal privacy on cloud computing.

Forensic Analysis of Telegram Desktop-based Applications using the National Institute of Justice (NIJ) Method

Abstract
 The development of telecommunications has increased very rapidly since the internet-based instant messaging service has spread rapidly to Indonesia. Telegram application is one of the growing and well-known application services in Indonesia, Desktop or smartphone-based Telegram applications, it is very possible to use digital crimes by using services, user personal information, or by hacking the Telegram application. This study explains the stages of investigation of cybercrime cases that occurred in desktop-based telegram. The method used for this research refers to the stage of investigation that was carried out in previous studies, namely using the National Institute of Justice (NIJ) method with the stages of the preparation stage, the collection stage, the examination stage, the analysis stage, and the reporting stage. The media used in this study is a desktop-based Telegram application that is synchronized with an Android-based Telegram. In this process, the location of the log file, cache, and digital proof image file was obtained in the conversation of a desktop-based Telegram application. Digital forensic evidence obtained is expected to strengthen evidence of criminal cases in court in the form of digital evidence analysis results.
 Keywords: Telecommunications, Digital Forensic, Telegram, Investigation, Cybercrime

SOS: Self‐organized secure framework for VANET

SummaryWhile authentication is a necessary requirement to provide security in vehicular ad hoc networks, user's personal information such as identity and location must be kept private. The reliance on road side units or centralized trusted authority nodes to provide security services is critical because both are vulnerable, thus cannot be accessed by all users, which mean security absence. In this paper, we introduce a self‐organized secure framework, deployed in vehicular ad hoc networks. The proposed framework solution is designed not only to provide an effective, integrated security and privacy‐preserving mechanism but also to retain the availability of all security services even if there are no road side units at all and/or the trusted authority node is compromised. A decentralized tier‐based security framework that depends on both trusted authority and some fully trusted nodes cooperated to distribute security services is presented. Our approach combines the useful features of both Shamir secret sharing with a trust‐based technique to ensure continuity of achieving all security services. Mathematical analysis of security issues that the proposed framework achieves as well as the availability of offering security services is provided. Proposed framework examination was done to show the performance in terms of storage, computation complexity, and communication overhead as well as its resilience against various types of attacks. Comparisons with different types of security schemes showed that the protocol developed gave better results in most comparison parameters while being unique ensuring continuity of security services delivery.

Traceable Method for Personal Information Registration Based on Blockchain

When a user registers a digital service, the service provider often asks for the user's personal information, such as name, phone number and so on. With the digital services gradually becoming an indispensable part of our lives, the abuse and misappropriation of personal information have caused people's attention to the protection of it. At present, The technical schemes for personal information protection mainly focus on preventing information leakage. We provide protection in a different way: propose a traceable method for personal information registration based on blockchain, which can distinguish the service provider obtained the information legally or illegally, by storing the personal information transaction records on the blockchain. The proposed method includes both direct and third-party personal information transaction scenarios. In different scenarios, the user will send the encrypted personal information data or authorization file to the service provider. After the transaction record is confirmed to be on the blockchain, the certification center will assist the service provider to decrypt the personal information. With this method, users can clearly understand which personal information has been delivered to which service providers. Moreover, the user's personal information data involving privacy are not stored on or transmitted through the blockchain. Hence, there is no additional risk of information disclosure, so as to achieve the purpose of personal information protection. Additionally, we analyze the proposed method using Kailar logic, and conduct a transaction performance simulation using NS-3. It shows that our method has properties of privacy, reliability, and accountability, and can meet the transaction performance requirements under the practical application scene.

Main trends in the field of protection of copyright and personal data in the Internet

The author explores the key legislative innovations in the sphere of legal regulation of relations involving use of Internet. The subject of this research is the legal relations with the use of modern communication relations, as well as their legal regulation. Special attention is given to the protection of copyright and personal information of Internet users. The article examines the recent legislative acts aimed at protection of personal data and copyright involving use of the modern communication technologies. The author examines statistical data pertaining to the use of personal information and content falling within the copyright in the Internet. A conclusion is made that in the Russian legal field personal data are fully controlled by the government, but the “large user data” do not appear therein, and in essence, displayed in a “grey zone”. Latest changes in legislation indicate desire of the government for “digital registration” of the largest possible number of citizens, as well as de-anonymization of the Internet users. The novelty of this research consists in analysis of the most recent normative acts in the area of legal regulation of copyright, preservation and usage of personal data involving the modern information and communication technologies. The author criticizes the existing legislation in the area of legal regulation of information exchange in the Internet with regards to violation of citizens’ right to privacy.

A Human Gait Recognition Against Information Theft in Smartphone using Residual Convolutional Neural Network

The genuine user of the smartphone is identified and information theft is prevented by continuous authentication, which is one of the most emerging features in biometrics application. A person is recognized by analysing the physiological or behavioural attributes is defined as biometrics. The physiological qualities include iris acknowledgment, impression of finger, palm and face geometry are used in the biometric validation frameworks. In the existing entry-point authentication techniques, a confidential information is lost because of internal attacks, while identifying the genuine user of the smartphone. Therefore, a biometric validation framework is designed in this research study to differentiate an authorized user by recognizing the gait. In order to identify the unauthorized smartphone access, a human gait recognition is carried out by implementing a Residual Convolutional Neural Network (RCNN) approach. A personal information of end user in smartphone is secured and presented a better solution from unauthorized access by proposed architecture. The performance of RCNN method is compared with the existing Deep Neural Network (DNN) in terms of classification accuracy. The simulation results showed that the RCNN method achieved 98.15% accuracy, where DNN achieved 95.67% accuracy on OU-ISIR dataset.

A Survey of Location Privacy Preservation in Social Internet of Vehicles

Social Internet of Vehicles (SIoV) is an emerging complex network where the features of Social Networks are applied to the SIoV system. User location data forms the basis for the implementation of SIoV functions. However, this type of data contains a large amount of user personal information, which may cause privacy leakage if it is stolen. Protecting the privacy of the user location can eliminate concerns about the leakage of user personal privacy data, increase users' viscosity, and help to contribute to the improvement of the SIoV system. This paper systematically analyzes the location privacy protection technology utilized in recent years in the field of SIoV, proposes three types of user data location privacy protection technology, and evaluates the performance of these technologies. We further present some potential future research directions for location privacy protection technology through the analysis and summary of existing work.

Social Media Analytics: The Next Big Thing?

The emergence of Internet and Social media has created huge opportunities for marketers to know their customers. User generated content and consumer generated media have brought social networking to a new altitude. People are eager to share what they think, what they prefer and companies are taking its advantage to the maximum. It is not only the one side of the coin which is being benefitted, consumers are also gaining in the form of improved information and opinion from users. For various companies, social media platforms have become primary source of personal information of users. Social media analytics help marketers to identify segments and know customer's appeal towards a particular brand. The present study focuses on the emergence of social media analytics and modelling. It has been observed that despite enormous benefits, companies in developing countries like India are unable to exploit the opportunity to the fullest. The present study is based on exploratory research design where extant review of literature has been carried out. It also focuses on the various methods used by marketers to derive decisions out of data available on the social media platforms. The study also includes a systematic arrangement of reviews and comments collected from a social media platform and a sentiment analysis is being conducted on the data collected. Finally this paper contributes to the area of SMA and induces marketers towards a wise use of social media analytics in their business.

Data acquisition methods using backup data decryption of Sony smartphones

As the storage capacity of smartphones increases, more user data such as call logs, SMS records, media data, and instant messages are stored in smartphones. Therefore, it is important in digital investigation to acquire smartphones containing the personal information of users. However, even when a prime suspect's smartphone is acquired, it is difficult to extract user data without obtaining root privilege. In this situation, smartphone backup data may be a valuable alternative to the extraction of user data. Using a smartphone backup, an investigator can extract most of the data stored in a smartphone including user data, with straightforward methods, and transfer them to a storage device such as an SD card, a USB, or a PC. Despite its convenience, backup data are hard to use as evidence, because backup data are encrypted using different methods depending on smartphone manufacturers, in order to protect user privacy.In this paper, we propose methods for decrypting encrypted backup data of Sony smartphones. In our analysis, we reverse-engineered the backup processes of the local backup and the PC backup provided by Sony smartphones, and analyzed the encryption methods applied to each set of backup data. In particular, we developed an algorithm for decrypting encrypted backup data on Sony smartphones, which we experimentally verified. As far as we know, this is the first research that has addressed the decryption of backup data on Sony smartphones.

Med-PPPHIS: Blockchain-Based Personal Healthcare Information System for National Physique Monitoring and Scientific Exercise Guiding.

The dissemination of electronic medical data among professional personnel has been perceived to be an important breakthrough for the discovery of new technologies and therapies for curing diseases. However, in the current medical data management, it is difficult to share medical data due to the fragmentation of medical data and the lack of effective sharing methods. On the other hand, the security of medical data is difficult to protect because the centralized data storage is vulnerable to attack and tampering. Therefore, we propose a model called Med-PPPHIS, which consists of a permission-less blockchain and a permissioned blockchain, named Med-DLattice, to serve the management of user's personal health information and form a chained protection mechanism for medical data. Med-DLattice features Directed Acyclic Graph (DAG) structure, where each account updates its Account-DAG asynchronously to other unrelated accounts. The Med-DLattice nodes can reach an efficient consensus with proposed DPoS-Quorum algorithm. Based on this model, by converting the medical data into on-chain tokens, a safe and efficient channel for data circulation is established, while the privacy of data is secured. We implement a prototype of Med-PPPHIS and introduce a blockchain-based closed-loop method for chronic disease management, which initially applies the model to national physique monitoring in Anhui Province, China. The performance of the model is evaluated by simulating 500 nodes on 25 AliCloud ECS virtual machines. Experimental result shows that Med-PPPHIS has low latency and high throughput, and the security analysis shows that the model is able to prevent Sybil attacks, DDoS attacks, etc.

An empirical study of purchase behaviour on social platforms

Purpose Social media users’ purchasing behaviour is yet to be fully understood by research. The purpose of this paper is to investigate how purchase intention is affected by social media user traits, cognitive factors (such as perceived control and trust) and individual beliefs, such as risk propensity and trustworthiness. Design/methodology/approach The authors propose and empirically test a model of purchase intention on social platforms. The study of over 500 active social media users finds the links between risk propensity, trust, technical efficacy and perceived control and explores the moderating effect of age and gender. Findings Purchase intention on social platforms is influenced by demographic factors, cognitive factors and beliefs. Both age and gender moderate the effects of beliefs and cognitive factors: age is a determinant of purchase intention for men, while beliefs are significant for younger women and cognitive factors are significant for older women. Research limitations/implications This study involved a cross-sectional design via online survey of social networking users. Gender differences in purchase intentions are found which are, in turn, influenced by age. Further empirical testing of social purchase intention could include less experienced users or non-users. Practical implications The results of this study provide guidance for SNS providers and technology developers in social networking commerce in terms of the different drivers of purchase intention. Originality/value Social media users’ purchasing behaviour is yet to be fully understood. The study shows that purchase intention antecedents vary between genders and age groups of users. The identified connection between users’ perceptions of social networking sites (SNS) usage of personal information and purchase behaviour has an impact on the likelihood of user engagement in social transactions.

Anonymous Single Sign-On With Proxy Re-Verification

An anonymous single sign-on (ASSO) scheme allows users to access multiple services anonymously using one credential. We propose a new ASSO scheme, where users can access services anonymously through the use of anonymous credentials and unlinkably through the provision of designated verifiers. Notably, verifiers cannot link a user's service requests even if they collude. The novelty is that when a designated verifier is unavailable, a central authority can authorize new verifiers to authenticate the user on behalf of the original verifier. Furthermore, a central verifier can also be authorized to de-anonymize users and trace their service requests. We formalize the scheme along with a security proof and provide an empirical evaluation of its performance. This scheme can be applied to smart ticketing where minimizing the collection of personal information of users is increasingly important to transport organizations due to privacy regulations such as general data protection regulations (GDPRs).

Comprehensive review and analysis of anti-malware apps for smartphones

The new and disruptive technologies for ensuring smartphone security are very limited and largely scattered. The available options and gaps in this research area must be analysed to provide valuable insights about the present technological environment. This work illustrates the research landscape by mapping the existing literature to a comprehensive taxonomy with four categories. The first category includes review and survey articles related to smartphone security. The second category includes papers on smartphone security solutions. The third category includes smartphone malware studies that examine the security aspects of smartphones and the threats posed by malware. The fourth category includes ranking, clustering and classification studies that classify malware based on their families or security risk levels. Several smartphone security apps have also been analysed and compared based on their mechanisms to identify their contents and distinguishing features by using several evaluation metrics and parameters. Two malware detection techniques, namely, machine-learning-based and non-machine-learning-based malware detection, are drawn from the review. The basic characteristics of this emerging field of research are discussed in the following aspects: (1) motivation behind the development of security measures for different smartphone operating system (Oss), (2) open challenges that compromise the usability and personal information of users and (3) recommendations for enhancing smartphone security. This work also reviews the functionalities and services of several anti-malware companies to fully reveal their security mechanisms, features and strategies. This work also highlights the open challenges and issues related to the evaluation and benchmarking of malware detection techniques to identify the best malware detection apps for smartphones.

Wikileaks and Digital Disclosure: Beclouded Muckraking, Profit-Taking and Self-Promoting

However the reader approaches the study of Julian Assange and WikiLeaks, there are numerous applied ethical issues implicated by their lives. The paper asks, among other questions, whether a new norm exists defining personal information and data as a “public good.” Under this view, anyone using the Internet seemingly has relinquished any direct (or exclusive) ownership claim to her data (personal information) or images of resemblance. In truth, part of data ownership is about control over our personhood, over the stories conveyed about us, and over what can be known about us. The meaning of the data is something that the person who generates the data can never really be detached from. To that extent, personal data represents intimate aspects of ourselves that we can't be separated from, no matter the motive of those seeking to partition the individual from the narrative. Loss of control over personal information is especially troubling in the current environment in which enterprises like Google cannot form, or maintain for extended periods, ethics boards charged with directing policy on extracting, bundling and sharing of personal information of users of SaaS and Web platforms. The fact that California in 2018 passed the CCPA or the EU its Data Privacy Regulation is cold comfort given the personal-data gold rush underway evidenced in the business of mining, bundling and sale of information. Remaining competitive, it seems, is more consequential than being respectful, or even cognizant, of the individual's ownership of her or his essential personhood. The essay is a vehicle for reflection for business ethicists about an essential moral issue for our time: the destructive social, as well as personal, impacts of compromised individual privacy in the digital-platform and social media realms.

An Internet Water Army Detection Supernetwork Model

The emergence of Internet water armies has strongly affected the information quality of online communication platforms, thus disrupting the order of the Internet. Accurate detection of Internet water armies is therefore of great significance. Based on the supernetwork theory, a new Internet water army detection model is proposed in this paper, in which a supernetwork with four layers is established, including social subnetwork, information subnetwork, psychological subnetwork, and negative keyword subnetwork. Then, personal information of users, dissemination process of information, the transformation process of different psychologies, the similarity between different keywords, and the connections between different subnetworks are considered in the model. Thus, nine composite indexes are proposed, the majority of which are used for the first time in detecting Internet water armies. A dataset selected from the largest online communication platform in China, the Weibo website, is used to test the performance of the model. Four existing water army detection models introduced in previous studies are used to provide a comparison analysis. The results show that our proposed model has better performance in terms of accuracy and stability than the other four existing models, which thanks to the employment of the supernetwork theory. We believe that our proposed model could be helpful for information researchers to further understand the complex nature of Internet water armies, as well as for the government to better manage the Internet.

De-anonymizing Scale-Free Social Networks by Using Spectrum Partitioning Method

Social network data is widely shared, forwarded and published to third parties, which led to the risks of privacy disclosure. Even thought the network provider always perturbs the data before publishing it, attackers can still recover anonymous data according to the collected auxiliary information. In this paper, we transform the problem of de-anonymization into node matching problem in graph, and the de-anonymization method can reduce the number of nodes to be matched at each time. In addition, we use spectrum partitioning method to divide the social graph into disjoint subgraphs, and it can effectively be applied to large-scale social networks and executed in parallel by using multiple processors. Through the analysis of the influence of power-law distribution on de-anonymization, we synthetically consider the structural and personal information of users which made the feature information of the user more practical.