Conceptual Paper: Sentience of Big Data towards User Privacy Concerns and Online Self-Disclosure Activities

2021 ◽  
Vol 2 (2) ◽  
Author(s):  
A Ismail ◽  
◽  
M R Hamzah ◽  
H Hussin ◽  
◽  
...  
Keyword(s):  
Big Data ◽  
Personal Data ◽  
User Privacy ◽  
Self Disclosure ◽  
Privacy Concerns ◽  
Perceived Risks ◽  
User Data ◽  
Conceptual Paper ◽  
Privacy Breaches ◽  
Different Sources

Big data allows widespread use and exchange of user data, and this will lead to the possibility of privacy breaches. Governments and corporations will incorporate personal data from different sources and learn a great deal about people and in turn, raise concerns about privacy. This paper will provide a conceptual understanding on the antecedents towards user privacy concerns and online self-disclosure activities, which are the knowledge and perceived risks of big data. In this paper, big data knowledge is hypothesized to decrease privacy concerns, meanwhile perceived risks is suggested to increase the outcome. Based on the framework, propositions are formulated as a basis for the study that will follow.

scholarly journals Digital Economy, Big Data and Competition Law

2019 ◽  
Vol 3 (1) ◽  
pp. 53-89
Author(s):  
Roberto Augusto Castellanos Pfeiffer
Keyword(s):  
Big Data ◽  
Data Protection ◽  
Personal Data ◽  
Competition Law ◽  
Digital Economy ◽  
Data Driven ◽  
Dominant Position ◽  
Privacy Concerns ◽  
Sense Data ◽  
Abuse Of Dominant Position

Big data has a very important role in the digital economy, because firms have accurate tools to collect, store, analyse, treat, monetise and disseminate voluminous amounts of data. Companies have been improving their revenues with information about the behaviour, preferences, needs, expectations, desires and evaluations of their consumers. In this sense, data could be considered as a productive input. The article focuses on the current discussion regarding the possible use of competition law and policy to address privacy concerns related to big data companies. The most traditional and powerful tool to deal with privacy concerns is personal data protection law. Notwithstanding, the article examines whether competition law should play an important role in data-driven markets where privacy is a key factor. The article suggests a new approach to the following antitrust concepts in cases related to big data platforms: assessment of market power, merger notification thresholds, measurement of merger effects on consumer privacy, and investigation of abuse of dominant position. In this context, the article analyses decisions of competition agencies which reviewed mergers in big data-driven markets, such as Google/DoubleClick, Facebook/ WhatsApp and Microsoft/LinkedIn. It also reviews investigations of alleged abuse of dominant position associated with big data, in particular the proceeding opened by the Bundeskartellamt against Facebook, in which the German antitrust authority prohibited the data processing policy imposed by Facebook on its users. The article concludes that it is important to harmonise the enforcement of competition, consumer and data protection polices in order to choose the proper way to protect the users of dominant platforms, maximising the benefits of the data-driven economy.

scholarly journals CEPTM: A Cross-Edge Model for Diverse Personalization Service and Topic Migration in MEC

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Hongchen Wu ◽  
Huaxiang Zhang ◽  
Lizhen Cui ◽  
Xinjun Wang
Keyword(s):  
Information Disclosure ◽  
Management Strategies ◽  
Mobile User ◽  
High Rate ◽  
Privacy Concern ◽  
Self Disclosure ◽  
Privacy Concerns ◽  
Individual Privacy ◽  
Computing Paradigm ◽  
User Data

For several reasons, the cloud computing paradigm, e.g., mobile edge computing (MEC), is suffering from the problem of privacy issues. MEC servers provide personalization services to mobile users for better QoE qualities, but the ongoing migrated data from the source edge server to the destination edge server cause users to have privacy concerns and unwillingness of self-disclosure, which further leads to a sparsity problem. As a result, personalization services ignore valuable user profiles across edges where users have accounts in and tend to predict users’ potential purchases with insufficient sources, thereby limiting further improvement of QoE through personalization of the contents. This paper proposes a novel model, called CEPTM, which (1) collects mobile user data across multiple MEC edge servers, (2) improves the users’ experience in personalization services by loading collected diverse data, and (3) lowers their privacy concern with the improved personalization. This model also reveals that famous topics in one edge server can migrate into several other edge servers with users’ favorite content tags and that the diverse types of items could increase the possibility of users accepting the personalization service. In the experiment section, we use exploratory factor analysis to mathematically evaluate the correlations among those factors that influence users’ information disclosure in the MEC network, and the results indicate that CEPTM (1) achieves a high rate of personalization acceptance due to the availability of more data as input and highly diverse personalization as output and (2) gains the users’ trust because it collects user data while respecting individual privacy concerns and providing better personalization. It outperforms a traditional personalization service that runs on a single-edge server. This paper provides new insights into MEC diverse personalization services and privacy problems, and researchers and personalization providers can apply this model to merge popular users’ like trends throughout the MEC edge servers and generate better data management strategies.

Impact of privacy, trust and user activity on intentions to share Facebook photos

2016 ◽  
Vol 14 (4) ◽  
pp. 364-382 ◽  
Author(s):  
Aqdas Malik ◽  
Kari Hiekkanen ◽  
Amandeep Dhir ◽  
Marko Nieminen
Keyword(s):  
Social Networking Sites ◽  
Research Model ◽  
User Privacy ◽  
Cross Sectional ◽  
Self Disclosure ◽  
Content Type ◽  
Privacy Concerns ◽  
Photo Sharing ◽  
The Impact ◽  
Privacy Issues

Purpose The popularity of Facebook photo sharing has not only seen a surge in the number of photos shared but also has raised various issues concerning user privacy and self-disclosure. Recent literature has documented the increasing interest of the research community in understanding various privacy issues concerning self-disclosures on Facebook. However, little is known about how different privacy issues, trust and activity influence users’ intentions to share photos on Facebook. To bridge this gap, a research model was developed and tested to better understand the impact of privacy concerns, privacy awareness and privacy-seeking on trust and actual photo sharing activity and subsequently on photo sharing intentions. This study aims to examine the consequences of various facets of privacy associated with photo sharing activity on Facebook. Design/methodology/approach A cross-sectional data from 378 respondents were collected and analysed using partial least squares modelling. Findings The results revealed a significant relationship between various aspects of privacy, including awareness and protective behaviour, with trust and activity. Furthermore, trust and users’ photo sharing activity significantly impact photo sharing intentions on Facebook. Originality/value This study contributes new knowledge concerning various privacy issues and their impact on photo sharing activity and trust. The study also proposes implications that are highly relevant for social networking sites, media agencies and organisations involved in safeguarding the privacy of online users.

scholarly journals UISTD: A Trust-Aware Model for Diverse Item Personalization in Social Sensing with Lower Privacy Intrusion

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4383 ◽  
Author(s):  
Hongchen Wu ◽  
Mingyang Li ◽  
Huaxiang Zhang
Keyword(s):  
Clustering Algorithm ◽  
Personal Information ◽  
Personal Data ◽  
Privacy Concern ◽  
User Privacy ◽  
Real World Data ◽  
Privacy Concerns ◽  
Social Sensing ◽  
The Core ◽  
Online Social Media

Privacy intrusion has become a major bottleneck for current trust-aware social sensing, since online social media allows anybody to largely disclose their personal information due to the proliferation of the Internet of Things (IoT). State-of-the-art social sensing still suffers from severe privacy threats since it collects users’ personal data and disclosure behaviors, which could raise user privacy concerns due to data integration for personalization. In this paper, we propose a trust-aware model, called the User and Item Similarity Model with Trust in Diverse Kinds (UISTD), to enhance the personalization of social sensing while reducing users’ privacy concerns. UISTD utilizes user-to-user similarities and item-to-item similarities to generate multiple kinds of personalized items with common tags. UISTD also applies a modified k-means clustering algorithm to select the core users among trust relationships, and the core users’ preferences and disclosure behaviors will be regarded as the predicted disclosure pattern. The experimental results on three real-world data sets demonstrate that target users are more likely to: (1) follow the core users’ interests on diverse kinds of items and disclosure behaviors, thereby outperforming the compared methods; and (2) disclose more information with lower intrusion awareness and privacy concern.

Suggestions for Achieving Balance Between Corporate Dataveillance and Employee Privacy Concerns

2019 ◽  
pp. 523-535
Author(s):  
Ordor Ngowari Rosette ◽  
Fatemeh Kazemeyni ◽  
Shaun Aghili ◽  
Sergey Butakov ◽  
Ron Ruhl
Keyword(s):  
Social Media ◽  
Big Data ◽  
Work Environment ◽  
Personal Data ◽  
Research Paper ◽  
Information Privacy ◽  
Technological Innovations ◽  
Employee Privacy ◽  
Privacy Concerns ◽  
Potential Risks

Big data, like most technological innovations, brings noticeable benefits as well potential risks. Dataveillance using big data is becoming another dimension in the increasing privacy concerns of the workforce. Such concerns emanate from the tension between the correct use of employee personal data and information privacy in big data within and outside the work environment. It has evolved as employees are becoming increasingly cognizant of the ways in which employers can use technologies to monitor social media activities, internet interactions, emails and other online activities outside the work environment. The objective of this research paper is to recommend a set of guidelines which will be mapped to COBIT 5 framework to help medium and large organizations balance the tension between the increasing potential of big data and employee dataveillance privacy concerns in workplaces.

Suggestions for Achieving Balance between Corporate Dataveillance and Employee Privacy Concerns

2017 ◽  
pp. 1863-1875
Author(s):  
Ordor Ngowari Rosette ◽  
Fatemeh Kazemeyni ◽  
Shaun Aghili ◽  
Sergey Butakov ◽  
Ron Ruhl
Keyword(s):  
Social Media ◽  
Big Data ◽  
Work Environment ◽  
Personal Data ◽  
Research Paper ◽  
Information Privacy ◽  
Technological Innovations ◽  
Employee Privacy ◽  
Privacy Concerns ◽  
Potential Risks

Big data, like most technological innovations, brings noticeable benefits as well potential risks. Dataveillance using big data is becoming another dimension in the increasing privacy concerns of the workforce. Such concerns emanate from the tension between the correct use of employee personal data and information privacy in big data within and outside the work environment. It has evolved as employees are becoming increasingly cognizant of the ways in which employers can use technologies to monitor social media activities, internet interactions, emails and other online activities outside the work environment. The objective of this research paper is to recommend a set of guidelines which will be mapped to COBIT 5 framework to help medium and large organizations balance the tension between the increasing potential of big data and employee dataveillance privacy concerns in workplaces.

scholarly journals Privacy in targeted advertising: A survey

2020 ◽  
Author(s):  
Imdad Ullah ◽  
Roksana Boreli ◽  
Salil S. Kanhere
Keyword(s):  
Private Information ◽  
Personal Data ◽  
Third Party ◽  
User Privacy ◽  
Research Issues ◽  
Privacy Concerns ◽  
Targeted Advertising ◽  
Privacy Threats ◽  
Measurement Analysis ◽  
Privacy Risks

Targeted advertising has transformed the marketing trend for any business by creating new opportunities for advertisers to reach prospective customers by delivering them personalised ads using an infrastructure of a variety of intermediary entities and technologies. The advertising and analytics companies collect, aggregate, process and trade a rich amount of user's personal data, which has prompted serious privacy concerns among individuals and organisations. This article presents a detailed survey of privacy risks including the information flow between advertising platform and ad/analytics networks, the profiling process, the advertising sources and criteria, the measurement analysis of targeted advertising based on user's interests and profiling context and ads delivery process in both in-app and in-browser targeted ads. We provide detailed discussion of challenges in preserving user privacy that includes privacy threats posed by the advertising and analytics companies, how private information is extracted and exchanged among various advertising entities, privacy threats from third-party tracking, re-identification of private information and associated privacy risks, in addition to, overview data and tracking sharing technologies. Following, we present various techniques for preserving user privacy and a comprehensive analysis of various proposals founded on those techniques and compare them based on the underlying architectures, the privacy mechanisms and the deployment scenarios. Finally we discuss some potential research challenges and open research issues.<br>

scholarly journals Privacy in targeted advertising: A survey

2020 ◽  
Author(s):  
Imdad Ullah ◽  
Roksana Boreli ◽  
Salil S. Kanhere
Keyword(s):  
Private Information ◽  
Personal Data ◽  
Third Party ◽  
User Privacy ◽  
Research Issues ◽  
Privacy Concerns ◽  
Targeted Advertising ◽  
Privacy Threats ◽  
Measurement Analysis ◽  
Privacy Risks

Targeted advertising has transformed the marketing trend for any business by creating new opportunities for advertisers to reach prospective customers by delivering them personalised ads using an infrastructure of a variety of intermediary entities and technologies. The advertising and analytics companies collect, aggregate, process and trade a rich amount of user's personal data, which has prompted serious privacy concerns among individuals and organisations. This article presents a detailed survey of privacy risks including the information flow between advertising platform and ad/analytics networks, the profiling process, the advertising sources and criteria, the measurement analysis of targeted advertising based on user's interests and profiling context and ads delivery process in both in-app and in-browser targeted ads. We provide detailed discussion of challenges in preserving user privacy that includes privacy threats posed by the advertising and analytics companies, how private information is extracted and exchanged among various advertising entities, privacy threats from third-party tracking, re-identification of private information and associated privacy risks, in addition to, overview data and tracking sharing technologies. Following, we present various techniques for preserving user privacy and a comprehensive analysis of various proposals founded on those techniques and compare them based on the underlying architectures, the privacy mechanisms and the deployment scenarios. Finally we discuss some potential research challenges and open research issues.<br>

Achieving Balance between Corporate Dataveillance and Employee Privacy Concerns

2016 ◽  
pp. 163-175
Author(s):  
Ordor Ngowari Rosette ◽  
Fatemeh Kazemeyni ◽  
Shaun Aghili ◽  
Sergey Butakov ◽  
Ron Ruhl
Keyword(s):  
Social Media ◽  
Big Data ◽  
Work Environment ◽  
Personal Data ◽  
Research Paper ◽  
Information Privacy ◽  
Technological Innovations ◽  
Employee Privacy ◽  
Privacy Concerns ◽  
Potential Risks

Big data, like most technological innovations, brings noticeable benefits as well potential risks. Dataveillance using big data is becoming another dimension in the increasing privacy concerns of the workforce. Such concerns emanate from the tension between the correct use of employee personal data and information privacy in big data within and outside the work environment. It has evolved as employees are becoming increasingly cognizant of the ways in which employers can use technologies to monitor social media activities, internet interactions, emails and other online activities outside the work environment. The objective of this research paper is to recommend a set of guidelines which will be mapped to COBIT 5 framework to help medium and large organizations balance the tension between the increasing potential of big data and employee dataveillance privacy concerns in workplaces.

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