Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 19119

Search results for: data privacy

19119 Reviewing Privacy Preserving Distributed Data Mining

Authors: Sajjad Baghernezhad, Saeideh Baghernezhad

Abstract:

Nowadays considering human involved in increasing data development some methods such as data mining to extract science are unavoidable. One of the discussions of data mining is inherent distribution of the data usually the bases creating or receiving such data belong to corporate or non-corporate persons and do not give their information freely to others. Yet there is no guarantee to enable someone to mine special data without entering in the owner’s privacy. Sending data and then gathering them by each vertical or horizontal software depends on the type of their preserving type and also executed to improve data privacy. In this study it was attempted to compare comprehensively preserving data methods; also general methods such as random data, coding and strong and weak points of each one are examined.

Keywords: data mining, distributed data mining, privacy protection, privacy preserving

Procedia PDF Downloads 383
19118 Choosing an Optimal Epsilon for Differentially Private Arrhythmia Analysis

Authors: Arin Ghazarian, Cyril Rakovski

Abstract:

Differential privacy has become the leading technique to protect the privacy of individuals in a database while allowing useful analysis to be done and the results to be shared. It puts a guarantee on the amount of privacy loss in the worst-case scenario. Differential privacy is not a toggle between full privacy and zero privacy. It controls the tradeoff between the accuracy of the results and the privacy loss using a single key parameter called

Keywords: arrhythmia, cardiology, differential privacy, ECG, epsilon, medi-cal data, privacy preserving analytics, statistical databases

Procedia PDF Downloads 68
19117 A Comprehensive Survey and Improvement to Existing Privacy Preserving Data Mining Techniques

Authors: Tosin Ige

Abstract:

Ethics must be a condition of the world, like logic. (Ludwig Wittgenstein, 1889-1951). As important as data mining is, it possess a significant threat to ethics, privacy, and legality, since data mining makes it difficult for an individual or consumer (in the case of a company) to control the accessibility and usage of his data. This research focuses on Current issues and the latest research and development on Privacy preserving data mining methods as at year 2022. It also discusses some advances in those techniques while at the same time highlighting and providing a new technique as a solution to an existing technique of privacy preserving data mining methods. This paper also bridges the wide gap between Data mining and the Web Application Programing Interface (web API), where research is urgently needed for an added layer of security in data mining while at the same time introducing a seamless and more efficient way of data mining.

Keywords: data, privacy, data mining, association rule, privacy preserving, mining technique

Procedia PDF Downloads 55
19116 Location Privacy Preservation of Vehicle Data In Internet of Vehicles

Authors: Ying Ying Liu, Austin Cooke, Parimala Thulasiraman

Abstract:

Internet of Things (IoT) has attracted a recent spark in research on Internet of Vehicles (IoV). In this paper, we focus on one research area in IoV: preserving location privacy of vehicle data. We discuss existing location privacy preserving techniques and provide a scheme for evaluating these techniques under IoV traffic condition. We propose a different strategy in applying Differential Privacy using k-d tree data structure to preserve location privacy and experiment on real world Gowalla data set. We show that our strategy produces differentially private data, good preservation of utility by achieving similar regression accuracy to the original dataset on an LSTM (Long Term Short Term Memory) neural network traffic predictor.

Keywords: differential privacy, internet of things, internet of vehicles, location privacy, privacy preservation scheme

Procedia PDF Downloads 59
19115 Online Shopping vs Privacy – Results of an Experimental Study

Authors: Andrzej Poszewiecki

Abstract:

The presented paper contributes to the experimental current of research on privacy. The question of privacy is being discussed at length at present, primarily among lawyers and politicians. However, the matter of privacy has been of interest for economists for some time as well. The valuation of privacy by people is of great importance now. This article is about how people valuate their privacy. An experimental method has been utilised in the conducted research – the survey was carried out among customers of an online store, and the studied issue was whether their readiness to sell their data (WTA) was different from the willingness to buy data back (WTP). The basic aim of this article is to analyse whether people shopping on the Internet differentiate their privacy depending on whether they protect or sell it. The achieved results indicate the presence of major differences in this respect, which do not always come up with the original expectations. The obtained results have supported the hypothesis that people are more willing to sell their data than to repurchase them. However, the hypothesis that the value of proposed remuneration affects the willingness to sell/buy back personal data (one’s privacy) has not been supported.

Keywords: privacy, experimental economics, behavioural economics, internet

Procedia PDF Downloads 199
19114 Platform-as-a-Service Sticky Policies for Privacy Classification in the Cloud

Authors: Maha Shamseddine, Amjad Nusayr, Wassim Itani

Abstract:

In this paper, we present a Platform-as-a-Service (PaaS) model for controlling the privacy enforcement mechanisms applied on user data when stored and processed in Cloud data centers. The proposed architecture consists of establishing user configurable ‘sticky’ policies on the Graphical User Interface (GUI) data-bound components during the application development phase to specify the details of privacy enforcement on the contents of these components. Various privacy classification classes on the data components are formally defined to give the user full control on the degree and scope of privacy enforcement including the type of execution containers to process the data in the Cloud. This not only enhances the privacy-awareness of the developed Cloud services, but also results in major savings in performance and energy efficiency due to the fact that the privacy mechanisms are solely applied on sensitive data units and not on all the user content. The proposed design is implemented in a real PaaS cloud computing environment on the Microsoft Azure platform.

Keywords: privacy enforcement, platform-as-a-service privacy awareness, cloud computing privacy

Procedia PDF Downloads 108
19113 Privacy Preserving Data Publishing Based on Sensitivity in Context of Big Data Using Hive

Authors: P. Srinivasa Rao, K. Venkatesh Sharma, G. Sadhya Devi, V. Nagesh

Abstract:

Privacy Preserving Data Publication is the main concern in present days because the data being published through the internet has been increasing day by day. This huge amount of data was named as Big Data by its size. This project deals the privacy preservation in the context of Big Data using a data warehousing solution called hive. We implemented Nearest Similarity Based Clustering (NSB) with Bottom-up generalization to achieve (v,l)-anonymity. (v,l)-Anonymity deals with the sensitivity vulnerabilities and ensures the individual privacy. We also calculate the sensitivity levels by simple comparison method using the index values, by classifying the different levels of sensitivity. The experiments were carried out on the hive environment to verify the efficiency of algorithms with Big Data. This framework also supports the execution of existing algorithms without any changes. The model in the paper outperforms than existing models.

Keywords: sensitivity, sensitive level, clustering, Privacy Preserving Data Publication (PPDP), bottom-up generalization, Big Data

Procedia PDF Downloads 205
19112 Offshore Outsourcing: Global Data Privacy Controls and International Compliance Issues

Authors: Michelle J. Miller

Abstract:

In recent year, there has been a rise of two emerging issues that impact the global employment and business market that the legal community must review closer: offshore outsourcing and data privacy. These two issues intersect because employment opportunities are shifting due to offshore outsourcing and some States, like the United States, anti-outsourcing legislation has been passed or presented to retain jobs within the country. In addition, the legal requirements to retain the privacy of data as a global employer extends to employees and third party service provides, including services outsourced to offshore locations. For this reason, this paper will review the intersection of these two issues with a specific focus on data privacy.

Keywords: outsourcing, data privacy, international compliance, multinational corporations

Procedia PDF Downloads 328
19111 A Privacy Protection Scheme Supporting Fuzzy Search for NDN Routing Cache Data Name

Authors: Feng Tao, Ma Jing, Guo Xian, Wang Jing

Abstract:

Named Data Networking (NDN) replaces IP address of traditional network with data name, and adopts dynamic cache mechanism. In the existing mechanism, however, only one-to-one search can be achieved because every data has a unique name corresponding to it. There is a certain mapping relationship between data content and data name, so if the data name is intercepted by an adversary, the privacy of the data content and user’s interest can hardly be guaranteed. In order to solve this problem, this paper proposes a one-to-many fuzzy search scheme based on order-preserving encryption to reduce the query overhead by optimizing the caching strategy. In this scheme, we use hash value to ensure the user’s query safe from each node in the process of search, so does the privacy of the requiring data content.

Keywords: NDN, order-preserving encryption, fuzzy search, privacy

Procedia PDF Downloads 355
19110 Secure Multiparty Computations for Privacy Preserving Classifiers

Authors: M. Sumana, K. S. Hareesha

Abstract:

Secure computations are essential while performing privacy preserving data mining. Distributed privacy preserving data mining involve two to more sites that cannot pool in their data to a third party due to the violation of law regarding the individual. Hence in order to model the private data without compromising privacy and information loss, secure multiparty computations are used. Secure computations of product, mean, variance, dot product, sigmoid function using the additive and multiplicative homomorphic property is discussed. The computations are performed on vertically partitioned data with a single site holding the class value.

Keywords: homomorphic property, secure product, secure mean and variance, secure dot product, vertically partitioned data

Procedia PDF Downloads 333
19109 Interpreting Privacy Harms from a Non-Economic Perspective

Authors: Christopher Muhawe, Masooda Bashir

Abstract:

With increased Internet Communication Technology(ICT), the virtual world has become the new normal. At the same time, there is an unprecedented collection of massive amounts of data by both private and public entities. Unfortunately, this increase in data collection has been in tandem with an increase in data misuse and data breach. Regrettably, the majority of data breach and data misuse claims have been unsuccessful in the United States courts for the failure of proof of direct injury to physical or economic interests. The requirement to express data privacy harms from an economic or physical stance negates the fact that not all data harms are physical or economic in nature. The challenge is compounded by the fact that data breach harms and risks do not attach immediately. This research will use a descriptive and normative approach to show that not all data harms can be expressed in economic or physical terms. Expressing privacy harms purely from an economic or physical harm perspective negates the fact that data insecurity may result into harms which run counter the functions of privacy in our lives. The promotion of liberty, selfhood, autonomy, promotion of human social relations and the furtherance of the existence of a free society. There is no economic value that can be placed on these functions of privacy. The proposed approach addresses data harms from a psychological and social perspective.

Keywords: data breach and misuse, economic harms, privacy harms, psychological harms

Procedia PDF Downloads 102
19108 Federated Learning in Healthcare

Authors: Ananya Gangavarapu

Abstract:

Convolutional Neural Networks (CNN) based models are providing diagnostic capabilities on par with the medical specialists in many specialty areas. However, collecting the medical data for training purposes is very challenging because of the increased regulations around data collections and privacy concerns around personal health data. The gathering of the data becomes even more difficult if the capture devices are edge-based mobile devices (like smartphones) with feeble wireless connectivity in rural/remote areas. In this paper, I would like to highlight Federated Learning approach to mitigate data privacy and security issues.

Keywords: deep learning in healthcare, data privacy, federated learning, training in distributed environment

Procedia PDF Downloads 51
19107 The Feminism of Data Privacy and Protection in Africa

Authors: Olayinka Adeniyi, Melissa Omino

Abstract:

The field of data privacy and data protection in Africa is still an evolving area, with many African countries yet to enact legislation on the subject. While African Governments are bringing their legislation to speed in this field, how patriarchy pervades every sector of African thought and manifests in society needs to be considered. Moreover, the laws enacted ought to be inclusive, especially towards women. This, in a nutshell, is the essence of data feminism. Data feminism is a new way of thinking about data science and data ethics that is informed by the ideas of intersectional feminism. Feminising data privacy and protection will involve thinking women, considering women in the issues of data privacy and protection, particularly in legislation, as is the case in this paper. The line of thought of women inclusion is not uncommon when even international and regional human rights specific for women only came long after the general human rights. The consideration is that these should have been inserted or rather included in the original general instruments in the first instance. Since legislation on data privacy is coming in this century, having seen the rights and shortcomings of earlier instruments, then the cue should be taken to ensure inclusive wholistic legislation for data privacy and protection in the first instance. Data feminism is arguably an area that has been scantily researched, albeit a needful one. With the spate of increase in the violence against women spiraling in the cyber world, compounding the issue of COVID-19 and the needful response of governments, and the effect of these on women and their rights, fast forward, the research on the feminism of data privacy and protection in Africa becomes inevitable. This paper seeks to answer the questions, what is data feminism in the African context, why is it important in the issue of data privacy and protection legislation; what are the laws, if any, existing on data privacy and protection in Africa, are they women inclusive, if not, why; what are the measures put in place for the privacy and protection of women in Africa, and how can this be made possible. The paper aims to investigate the issue of data privacy and protection in Africa, the legal framework, and the protection or provision that it has for women if any. It further aims to research the importance and necessity of feminizing data privacy and protection, the effect of lack of it, the challenges or bottlenecks in attaining this feat and the possibilities of accessing data privacy and protection for African women. The paper also researches the emerging practices of data privacy and protection of women in other jurisprudences. It approaches the research through the methodology of review of papers, analysis of laws, and reports. It seeks to contribute to the existing literature in the field and is explorative in its suggestion. It suggests a draft of some clauses to make any data privacy and protection legislation women inclusive. It would be useful for policymaking, academic, and public enlightenment.

Keywords: feminism, women, law, data, Africa

Procedia PDF Downloads 66
19106 Privacy-Preserving Model for Social Network Sites to Prevent Unwanted Information Diffusion

Authors: Sanaz Kavianpour, Zuraini Ismail, Bharanidharan Shanmugam

Abstract:

Social Network Sites (SNSs) can be served as an invaluable platform to transfer the information across a large number of individuals. A substantial component of communicating and managing information is to identify which individual will influence others in propagating information and also whether dissemination of information in the absence of social signals about that information will be occurred or not. Classifying the final audience of social data is difficult as controlling the social contexts which transfers among individuals are not completely possible. Hence, undesirable information diffusion to an unauthorized individual on SNSs can threaten individuals’ privacy. This paper highlights the information diffusion in SNSs and moreover it emphasizes the most significant privacy issues to individuals of SNSs. The goal of this paper is to propose a privacy-preserving model that has urgent regards with individuals’ data in order to control availability of data and improve privacy by providing access to the data for an appropriate third parties without compromising the advantages of information sharing through SNSs.

Keywords: anonymization algorithm, classification algorithm, information diffusion, privacy, social network sites

Procedia PDF Downloads 233
19105 On Privacy-Preserving Search in the Encrypted Domain

Authors: Chun-Shien Lu

Abstract:

Privacy-preserving query has recently received considerable attention in the signal processing and multimedia community. It is also a critical step in wireless sensor network for retrieval of sensitive data. The purposes of privacy-preserving query in both the areas of signal processing and sensor network are the same, but the similarity and difference of the adopted technologies are not fully explored. In this paper, we first review the recently developed methods of privacy-preserving query, and then describe in a comprehensive manner what we can learn from the mutual of both areas.

Keywords: encryption, privacy-preserving, search, security

Procedia PDF Downloads 135
19104 Privacy Label: An Alternative Approach to Present Privacy Policies from Online Services to the User

Authors: Diego Roberto Goncalves De Pontes, Sergio Donizetti Zorzo

Abstract:

Studies show that most users do not read privacy policies from the online services they use. Some authors claim that one of the main causes of this is that policies are long and usually hard to understand, which make users lose interest in reading them. In this scenario, users may agree with terms without knowing what kind of data is being collected and why. Given that, we aimed to develop a model that would present the privacy policies contents in an easy and graphical way for the user to understand. We call it the Privacy Label. Using information recovery techniques, we propose an architecture that is able to extract information about what kind of data is being collected and to what end in the policies and show it to the user in an automated way. To assess our model, we calculated the precision, recall and f-measure metrics on the information extracted by our technique. The results for each metric were 68.53%, 85.61% e 76,13%, respectively, making it possible for the final user to understand which data was being collected without reading the whole policy. Also, our proposal can facilitate the notice-and-choice by presenting privacy policy information in an alternative way for online users.

Keywords: privacy, policies, user behavior, computer human interaction

Procedia PDF Downloads 224
19103 Strengthening Legal Protection of Personal Data through Technical Protection Regulation in Line with Human Rights

Authors: Tomy Prihananto, Damar Apri Sudarmadi

Abstract:

Indonesia recognizes the right to privacy as a human right. Indonesia provides legal protection against data management activities because the protection of personal data is a part of human rights. This paper aims to describe the arrangement of data management and data management in Indonesia. This paper is a descriptive research with qualitative approach and collecting data from literature study. Results of this paper are comprehensive arrangement of data that have been set up as a technical requirement of data protection by encryption methods. Arrangements on encryption and protection of personal data are mutually reinforcing arrangements in the protection of personal data. Indonesia has two important and immediately enacted laws that provide protection for the privacy of information that is part of human rights.

Keywords: Indonesia, protection, personal data, privacy, human rights, encryption

Procedia PDF Downloads 88
19102 HPPDFIM-HD: Transaction Distortion and Connected Perturbation Approach for Hierarchical Privacy Preserving Distributed Frequent Itemset Mining over Horizontally-Partitioned Dataset

Authors: Fuad Ali Mohammed Al-Yarimi

Abstract:

Many algorithms have been proposed to provide privacy preserving in data mining. These protocols are based on two main approaches named as: the perturbation approach and the Cryptographic approach. The first one is based on perturbation of the valuable information while the second one uses cryptographic techniques. The perturbation approach is much more efficient with reduced accuracy while the cryptographic approach can provide solutions with perfect accuracy. However, the cryptographic approach is a much slower method and requires considerable computation and communication overhead. In this paper, a new scalable protocol is proposed which combines the advantages of the perturbation and distortion along with cryptographic approach to perform privacy preserving in distributed frequent itemset mining on horizontally distributed data. Both the privacy and performance characteristics of the proposed protocol are studied empirically.

Keywords: anonymity data, data mining, distributed frequent itemset mining, gaussian perturbation, perturbation approach, privacy preserving data mining

Procedia PDF Downloads 423
19101 An Analysis of Privacy and Security for Internet of Things Applications

Authors: Dhananjay Singh, M. Abdullah-Al-Wadud

Abstract:

The Internet of Things is a concept of a large scale ecosystem of wireless actuators. The actuators are defined as things in the IoT, those which contribute or produces some data to the ecosystem. However, ubiquitous data collection, data security, privacy preserving, large volume data processing, and intelligent analytics are some of the key challenges into the IoT technologies. In order to solve the security requirements, challenges and threats in the IoT, we have discussed a message authentication mechanism for IoT applications. Finally, we have discussed data encryption mechanism for messages authentication before propagating into IoT networks.

Keywords: Internet of Things (IoT), message authentication, privacy, security

Procedia PDF Downloads 273
19100 A Systematic Review on Challenges in Big Data Environment

Authors: Rimmy Yadav, Anmol Preet Kaur

Abstract:

Big Data has demonstrated the vast potential in streamlining, deciding, spotting business drifts in different fields, for example, producing, fund, Information Technology. This paper gives a multi-disciplinary diagram of the research issues in enormous information and its procedures, instruments, and system identified with the privacy, data storage management, network and energy utilization, adaptation to non-critical failure and information representations. Other than this, result difficulties and openings accessible in this Big Data platform have made.

Keywords: big data, privacy, data management, network and energy consumption

Procedia PDF Downloads 205
19099 BigCrypt: A Probable Approach of Big Data Encryption to Protect Personal and Business Privacy

Authors: Abdullah Al Mamun, Talal Alkharobi

Abstract:

As data size is growing up, people are became more familiar to store big amount of secret information into cloud storage. Companies are always required to need transfer massive business files from one end to another. We are going to lose privacy if we transmit it as it is and continuing same scenario repeatedly without securing the communication mechanism means proper encryption. Although asymmetric key encryption solves the main problem of symmetric key encryption but it can only encrypt limited size of data which is inapplicable for large data encryption. In this paper we propose a probable approach of pretty good privacy for encrypt big data using both symmetric and asymmetric keys. Our goal is to achieve encrypt huge collection information and transmit it through a secure communication channel for committing the business and personal privacy. To justify our method an experimental dataset from three different platform is provided. We would like to show that our approach is working for massive size of various data efficiently and reliably.

Keywords: big data, cloud computing, cryptography, hadoop, public key

Procedia PDF Downloads 233
19098 Evaluation of Australian Open Banking Regulation: Balancing Customer Data Privacy and Innovation

Authors: Suman Podder

Abstract:

As Australian ‘Open Banking’ allows customers to share their financial data with accredited Third-Party Providers (‘TPPs’), it is necessary to evaluate whether the regulators have achieved the balance between protecting customer data privacy and promoting data-related innovation. Recognising the need to increase customers’ influence on their own data, and the benefits of data-related innovation, the Australian Government introduced ‘Consumer Data Right’ (‘CDR’) to the banking sector through Open Banking regulation. Under Open Banking, TPPs can access customers’ banking data that allows the TPPs to tailor their products and services to meet customer needs at a more competitive price. This facilitated access and use of customer data will promote innovation by providing opportunities for new products and business models to emerge and grow. However, the success of Open Banking depends on the willingness of the customers to share their data, so the regulators have augmented the protection of data by introducing new privacy safeguards to instill confidence and trust in the system. The dilemma in policymaking is that, on the one hand, lenient data privacy laws will help the flow of information, but at the risk of individuals’ loss of privacy, on the other hand, stringent laws that adequately protect privacy may dissuade innovation. Using theoretical and doctrinal methods, this paper examines whether the privacy safeguards under Open Banking will add to the compliance burden of the participating financial institutions, resulting in the undesirable effect of stifling other policy objectives such as innovation. The contribution of this research is three-fold. In the emerging field of customer data sharing, this research is one of the few academic studies on the objectives and impact of Open Banking in the Australian context. Additionally, Open Banking is still in the early stages of implementation, so this research traces the evolution of Open Banking through policy debates regarding the desirability of customer data-sharing. Finally, the research focuses not only on the customers’ data privacy and juxtaposes it with another important objective of promoting innovation, but it also highlights the critical issues facing the data-sharing regime. This paper argues that while it is challenging to develop a regulatory framework for protecting data privacy without impeding innovation and jeopardising yet unknown opportunities, data privacy and innovation promote different aspects of customer welfare. This paper concludes that if a regulation is appropriately designed and implemented, the benefits of data-sharing will outweigh the cost of compliance with the CDR.

Keywords: consumer data right, innovation, open banking, privacy safeguards

Procedia PDF Downloads 45
19097 Privacy Preserving in Association Rule Mining on Horizontally Partitioned Database

Authors: Manvar Sagar, Nikul Virpariya

Abstract:

The advancement in data mining techniques plays an important role in many applications. In context of privacy and security issues, the problems caused by association rule mining technique are investigated by many research scholars. It is proved that the misuse of this technique may reveal the database owner’s sensitive and private information to others. Many researchers have put their effort to preserve privacy in Association Rule Mining. Amongst the two basic approaches for privacy preserving data mining, viz. Randomization based and Cryptography based, the later provides high level of privacy but incurs higher computational as well as communication overhead. Hence, it is necessary to explore alternative techniques that improve the over-heads. In this work, we propose an efficient, collusion-resistant cryptography based approach for distributed Association Rule mining using Shamir’s secret sharing scheme. As we show from theoretical and practical analysis, our approach is provably secure and require only one time a trusted third party. We use secret sharing for privately sharing the information and code based identification scheme to add support against malicious adversaries.

Keywords: Privacy, Privacy Preservation in Data Mining (PPDM), horizontally partitioned database, EMHS, MFI, shamir secret sharing

Procedia PDF Downloads 322
19096 Users’ Information Disclosure Determinants in Social Networking Sites: A Systematic Literature Review

Authors: Wajdan Al Malwi, Karen Renaud, Lewis Mackenzie

Abstract:

The privacy paradox describes a phenomenon whereby there is no connection between stated privacy concerns and privacy behaviours. We need to understand the underlying reasons for this paradox if we are to help users to preserve their privacy more effectively. In particular, the Social Networking System (SNS) domain offers a rich area of investigation due to the risks of unwise information disclosure decisions. Our study thus aims to untangle the complicated nature and underlying mechanisms of online privacy-related decisions in SNSs. In this paper, we report on the findings of a Systematic Literature Review (SLR) that revealed a number of factors that are likely to influence online privacy decisions. Our deductive analysis approach was informed by Communicative Privacy Management (CPM) theory. We uncovered a lack of clarity around privacy attitudes and their link to behaviours, which makes it challenging to design privacy-protecting SNS platforms and to craft legislation to ensure that users’ privacy is preserved.

Keywords: privacy paradox, self-disclosure, privacy attitude, privacy behavior, social networking sites

Procedia PDF Downloads 56
19095 Privacy Preservation Concerns and Information Disclosure on Social Networks: An Ongoing Research

Authors: Aria Teimourzadeh, Marc Favier, Samaneh Kakavand

Abstract:

The emergence of social networks has revolutionized the exchange of information. Every behavior on these platforms contributes to the generation of data known as social network data that are processed, stored and published by the social network service providers. Hence, it is vital to investigate the role of these platforms in user data by considering the privacy measures, especially when we observe the increased number of individuals and organizations engaging with the current virtual platforms without being aware that the data related to their positioning, connections and behavior is uncovered and used by third parties. Performing analytics on social network datasets may result in the disclosure of confidential information about the individuals or organizations which are the members of these virtual environments. Analyzing separate datasets can reveal private information about relationships, interests and more, especially when the datasets are analyzed jointly. Intentional breaches of privacy is the result of such analysis. Addressing these privacy concerns requires an understanding of the nature of data being accumulated and relevant data privacy regulations, as well as motivations for disclosure of personal information on social network platforms. Some significant points about how user's online information is controlled by the influence of social factors and to what extent the users are concerned about future use of their personal information by the organizations, are highlighted in this paper. Firstly, this research presents a short literature review about the structure of a network and concept of privacy in Online Social Networks. Secondly, the factors of user behavior related to privacy protection and self-disclosure on these virtual communities are presented. In other words, we seek to demonstrates the impact of identified variables on user information disclosure that could be taken into account to explain the privacy preservation of individuals on social networking platforms. Thirdly, a few research directions are discussed to address this topic for new researchers.

Keywords: information disclosure, privacy measures, privacy preservation, social network analysis, user experience

Procedia PDF Downloads 200
19094 An Investigation of the Relationship Between Privacy Crisis, Public Discourse on Privacy, and Key Performance Indicators at Facebook (2004–2021)

Authors: Prajwal Eachempati, Laurent Muzellec, Ashish Kumar Jha

Abstract:

We use Facebook as a case study to investigate the complex relationship between the firm’s public discourse (and actions) surrounding data privacy and the performance of a business model based on monetizing user’s data. We do so by looking at the evolution of public discourse over time (2004–2021) and relate topics to revenue and stock market evolution Drawing from archival sources like Zuckerberg We use LDA topic modelling algorithm to reveal 19 topics regrouped in 6 major themes. We first show how, by using persuasive and convincing language that promises better protection of consumer data usage, but also emphasizes greater user control over their own data, the privacy issue is being reframed as one of greater user control and responsibility. Second, we aim to understand and put a value on the extent to which privacy disclosures have a potential impact on the financial performance of social media firms. There we found significant relationship between the topics pertaining to privacy and social media/technology, sentiment score and stock market prices. Revenue is found to be impacted by topics pertaining to politics and new product and service innovations while number of active users is not impacted by the topics unless moderated by external control variables like Return on Assets and Brand Equity.

Keywords: public discourses, data protection, social media, privacy, topic modeling, business models, financial performance

Procedia PDF Downloads 6
19093 Digital Privacy Legislation Awareness

Authors: Henry Foulds, Magda Huisman, Gunther R. Drevin

Abstract:

Privacy is regarded as a fundamental human right and it is clear that the study of digital privacy is an important field. Digital privacy is influenced by new and constantly evolving technologies and this continuous change makes it hard to create legislation to protect people’s privacy from being exploited by misuse of these technologies.

This study aims to benefit digital privacy legislation efforts by evaluating the awareness and perceived importance of digital privacy legislation among computer science students. The chosen fixed variables for the population are study year and gamer classification.

The use of location based services in mobile applications and games are a concern for digital privacy. For this reason the study focused on computer science students as they have a high likelihood to use and develop this type of software. Surveys were used to evaluate awareness and perceived importance of digital privacy legislation.

The results of the study show that privacy legislation and awareness of privacy legislation are important to people. The perception of the importance of privacy legislation increases with academic experience. Awareness of privacy legislation increases from non-gamers to pro gamers. 

Keywords: digital privacy, legislation awareness, gaming, privacy legislation

Procedia PDF Downloads 261
19092 Secure Network Coding-Based Named Data Network Mutual Anonymity Transfer Protocol

Authors: Tao Feng, Fei Xing, Ye Lu, Jun Li Fang

Abstract:

NDN is a kind of future Internet architecture. Due to the NDN design introduces four privacy challenges,Many research institutions began to care about the privacy issues of naming data network(NDN).In this paper, we are in view of the major NDN’s privacy issues to investigate privacy protection,then put forwards more effectively anonymous transfer policy for NDN.Firstly,based on mutual anonymity communication for MP2P networks,we propose NDN mutual anonymity protocol.Secondly,we add interest package authentication mechanism in the protocol and encrypt the coding coefficient, security of this protocol is improved by this way.Finally, we proof the proposed anonymous transfer protocol security and anonymity.

Keywords: NDN, mutual anonymity, anonymous routing, network coding, authentication mechanism

Procedia PDF Downloads 342
19091 A Study of Predicting Judgments on Causes of Online Privacy Invasions: Based on U.S Judicial Cases

Authors: Minjung Park, Sangmi Chai, Myoung Jun Lee

Abstract:

Since there are growing concerns on online privacy, enterprises could involve various personal privacy infringements cases resulting legal causations. For companies that are involving online business, it is important for them to pay extra attentions to protect users’ privacy. If firms can aware consequences from possible online privacy invasion cases, they can more actively prevent future online privacy infringements. This study attempts to predict the probability of ruling types caused by various invasion cases under U.S Personal Privacy Act. More specifically, this research explores online privacy invasion cases which was sentenced guilty to identify types of criminal punishments such as penalty, imprisonment, probation as well as compensation in civil cases. Based on the 853 U.S judicial cases ranged from January, 2000 to May, 2016, which related on data privacy, this research examines the relationship between personal information infringements cases and adjudications. Upon analysis results of 41,724 words extracted from 853 regal cases, this study examined online users’ privacy invasion cases to predict the probability of conviction for a firm as an offender in both of criminal and civil law. This research specifically examines that a cause of privacy infringements and a judgment type, whether it leads a civil or criminal liability, from U.S court. This study applies network text analysis (NTA) for data analysis, which is regarded as a useful method to discover embedded social trends within texts. According to our research results, certain online privacy infringement cases caused by online spamming and adware have a high possibility that firms are liable in the case. Our research results provide meaningful insights to academia as well as industry. First, our study is providing a new insight by applying Big Data analytics to legal cases so that it can predict the cause of invasions and legal consequences. Since there are few researches applying big data analytics in the domain of law, specifically in online privacy, this study suggests new area that future studies can explore. Secondly, this study reflects social influences, such as a development of privacy invasion technologies and changes of users’ level of awareness of online privacy on judicial cases analysis by adopting NTA method. Our research results indicate that firms need to improve technical and managerial systems to protect users’ online privacy to avoid negative legal consequences.

Keywords: network text analysis, online privacy invasions, personal information infringements, predicting judgements

Procedia PDF Downloads 151
19090 Assistive Individual Privacy Modeling (AIPM) for Content Disclosure in Online Social Networks

Authors: Rezaul Raju, Syed M. S. Reza, Mohammad Hoque, Thamar Solorio, Ernst L. Leiss

Abstract:

Characterizing behavior and modeling privacy in the context of an individual user or a group of users is an interesting problem in Online Social Networks. Although significant research has been conducted on behavioral analysis for a group of users regarding group privacy policies, the behavioral analysis and privacy modeling for an individual user hasn’t been explored extensively. Nowadays, people interact with multiple social interfaces and expose a wide range of their personal data. These environments let users upload content (text, photo, video, etc.) and interact with other users’ contents. In current practice, the user has to take absolute responsibility for privacy while sharing or interacting with content. With a typical user grouping, the social sites offer limited privacy options to restrict the audience while uploading content. This process requires a user’s appropriate privacy selection, adequate knowledge about underlying privacy policy, and familiarity with the privacy setup interface. Interesting to note is that it completely lacks any system assistance or insight to aid the user in selecting a proper audience for the content. In this paper, we propose a method for characterizing and identifying an individual user’s behavior based on his/her prior interactions (content disclosure) in social networks. We apply machine learning techniques to analyze individual users’ social exposures and identify personalized privacy preferences. Exposed privacy is chosen as an attribute to infer privacy to similar content. Our experiments show that the proposed privacy classifier with transfer learning performs well regarding personalized privacy preference. We apply this personalized privacy model to make effective privacy recommendations for new content disclosures.

Keywords: personalized privacy, deep learning, information security, online social networks

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