Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 521

Search results for: privacy preserving

521 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
520 Reviewing Privacy Preserving Distributed Data Mining

Authors: Sajjad Baghernezhad, Saeideh Baghernezhad

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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
519 A Comprehensive Survey and Improvement to Existing Privacy Preserving Data Mining Techniques

Authors: Tosin Ige

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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 56
518 Choosing an Optimal Epsilon for Differentially Private Arrhythmia Analysis

Authors: Arin Ghazarian, Cyril Rakovski

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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
517 Secure Multiparty Computations for Privacy Preserving Classifiers

Authors: M. Sumana, K. S. Hareesha

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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 337
516 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 424
515 Location Privacy Preservation of Vehicle Data In Internet of Vehicles

Authors: Ying Ying Liu, Austin Cooke, Parimala Thulasiraman

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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 61
514 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

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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
513 Privacy-Preserving Location Sharing System with Client/Server Architecture in Mobile Online Social Network

Authors: Xi Xiao, Chunhui Chen, Xinyu Liu, Guangwu Hu, Yong Jiang

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Location sharing is a fundamental service in mobile Online Social Networks (mOSNs), which raises significant privacy concerns in recent years. Now, most location-based service applications adopt client/server architecture. In this paper, a location sharing system, named CSLocShare, is presented to provide flexible privacy-preserving location sharing with client/server architecture in mOSNs. CSLocShare enables location sharing between both trusted social friends and untrusted strangers without the third-party server. In CSLocShare, Location-Storing Social Network Server (LSSNS) provides location-based services but do not know the users’ real locations. The thorough analysis indicates that the users’ location privacy is protected. Meanwhile, the storage and the communication cost are saved. CSLocShare is more suitable and effective in reality.

Keywords: mobile online social networks, client/server architecture, location sharing, privacy-preserving

Procedia PDF Downloads 240
512 A Privacy Protection Scheme Supporting Fuzzy Search for NDN Routing Cache Data Name

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

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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 359
511 Privacy-Preserving Model for Social Network Sites to Prevent Unwanted Information Diffusion

Authors: Sanaz Kavianpour, Zuraini Ismail, Bharanidharan Shanmugam

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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 234
510 Blockchain Technology Applications in Patient Tracking Systems Regarding Privacy-Preserving Concerns and COVID-19 Pandemic

Authors: Farbod Behnaminia, Saeed Samet

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The COVID-19 pandemic has paralyzed many lives until a vaccine has been available, which caused the so-called “new normal”. According to the World Health Organization (WHO), COVID-19 is an infectious disease. It can cause significant illness or death in anyone. Governments and health officials tried to impose rules and regulations to avoid and slow down transmission. Therefore, software engineers worldwide developed applications to trace and track patients’ movements and notify others, mainly using Bluetooth. In this way, everyone could be informed whether they came in close contact with someone who has COVID-19 and takes proper safety precautions. Because most of the applications use technologies that can potentially reveal the user’s identity and location, researchers have debated privacy preservation and how to improve user privacy during such pandemics. Thanks to the distributed ledger technology (DLT), there have been some proposed methods to develop privacy-preserving patient tracking systems in the last two years. As an instance of the DLT, blockchain is like a decentralized peer-to-peer database that maintains a record of transactions. Transactions are immutable, transparent, and anonymous in this system. We conducted a comprehensive evaluation of the literature by looking for papers in the relevant field and dividing them into pre- and post-pandemic systems. Additionally, we discussed the many uses of blockchain technology in pandemic control. We found that two major obstacles facing blockchain implementation across many healthcare systems are scalability and privacy. The Polkadot platform is presented, along with a review of its efficacy in tackling current concerns. A more scalable healthcare system is achievable in the near future using Polkadot as well as a much more privacy-preserving environment.

Keywords: blockchain, electronic record management, EHR, privacy-preserving, patient tracking, COVID-19, trust and confidence, Polkadot

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509 A New Protocol Ensuring Users' Privacy in Pervasive Environment

Authors: Mohammed Nadir Djedid, Abdallah Chouarfia

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Transparency of the system and its integration into the natural environment of the user are some of the important features of pervasive computing. But these characteristics that are considered as the strongest points of pervasive systems are also their weak points in terms of the user’s privacy. The privacy in pervasive systems involves more than the confidentiality of communications and concealing the identity of virtual users. The physical presence and behavior of the user in the pervasive space cannot be completely hidden and can reveal the secret of his/her identity and affect his/her privacy. This paper shows that the application of major techniques for protecting the user’s privacy still insufficient. A new solution named Shadow Protocol is proposed, which allows the users to authenticate and interact with the surrounding devices within an ubiquitous computing environment while preserving their privacy.

Keywords: pervasive systems, identification, authentication, privacy

Procedia PDF Downloads 349
508 Privacy Preserving in Association Rule Mining on Horizontally Partitioned Database

Authors: Manvar Sagar, Nikul Virpariya

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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 323
507 A Multilevel Authentication Protocol: MAP in VANET for Human Safety

Authors: N. Meddeb, A. M. Makhlouf, M. A. Ben Ayed

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Due to the real-time requirement of message in Vehicular Ad hoc NETworks (VANET), it is necessary to authenticate vehicles to achieve security, efficiency, and conditional privacy-preserving. Privacy is of utmost relevance in VANETs. For this reason, we have proposed a new protocol called ‘Multilevel Authentication Protocol’ (MAP) that considers different vehicle categories. The proposed protocol is based on our Multilevel Authentication protocol for Vehicular networks (MAVnet). But the MAP leads to human safety, where the priority is given to the ambulance vehicles. For evaluation, we used the Java language to develop a demo application and deployed it on the Network Security Simulation (Nessi2). Compared with existing authentication protocols, MAP markedly enhance the communication overhead and decreases the delay of exchanging messages while preserving conditional privacy.

Keywords: Vehicular Ad hoc NETworks (VANET), vehicle categories, safety, databases, privacy, authentication, throughput, delay

Procedia PDF Downloads 208
506 Preserving Privacy in Workflow Delegation Models

Authors: Noha Nagy, Hoda Mokhtar, Mohamed El Sherkawi

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The popularity of workflow delegation models and the increasing number of workflow provenance-aware systems motivate the need for finding more strict delegation models. Such models combine different approaches for enhanced security and respecting workflow privacy. Although modern enterprises seek conformance to workflow constraints to ensure correctness of their work, these constraints pose a threat to security, because these constraints can be good seeds for attacking privacy even in secure models. This paper introduces a comprehensive Workflow Delegation Model (WFDM) that utilizes provenance and workflow constraints to prevent malicious delegate from attacking workflow privacy as well as extending the delegation functionalities. In addition, we argue the need for exploiting workflow constraints to improve workflow security models.

Keywords: workflow delegation models, secure workflow, workflow privacy, workflow provenance

Procedia PDF Downloads 179
505 An Analysis of Privacy and Security for Internet of Things Applications

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

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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 274
504 Isolation Preserving Medical Conclusion Hold Structure via C5 Algorithm

Authors: Swati Kishor Zode, Rahul Ambekar

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Data mining is the extraction of fascinating examples on the other hand information from enormous measure of information and choice is made as indicated by the applicable information extracted. As of late, with the dangerous advancement in internet, stockpiling of information and handling procedures, privacy preservation has been one of the major (higher) concerns in data mining. Various techniques and methods have been produced for protection saving data mining. In the situation of Clinical Decision Support System, the choice is to be made on the premise of the data separated from the remote servers by means of Internet to diagnose the patient. In this paper, the fundamental thought is to build the precision of Decision Support System for multiple diseases for different maladies and in addition protect persistent information while correspondence between Clinician side (Client side) also, the Server side. A privacy preserving protocol for clinical decision support network is proposed so that patients information dependably stay scrambled amid diagnose prepare by looking after the accuracy. To enhance the precision of Decision Support System for various malady C5.0 classifiers and to save security, a Homomorphism encryption algorithm Paillier cryptosystem is being utilized.

Keywords: classification, homomorphic encryption, clinical decision support, privacy

Procedia PDF Downloads 261
503 Users’ Information Disclosure Determinants in Social Networking Sites: A Systematic Literature Review

Authors: Wajdan Al Malwi, Karen Renaud, Lewis Mackenzie

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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 60
502 Digital Privacy Legislation Awareness

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

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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
501 Online Shopping vs Privacy – Results of an Experimental Study

Authors: Andrzej Poszewiecki

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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 201
500 Platform-as-a-Service Sticky Policies for Privacy Classification in the Cloud

Authors: Maha Shamseddine, Amjad Nusayr, Wassim Itani

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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
499 Privacy Policy Prediction for Uploaded Image on Content Sharing Sites

Authors: Pallavi Mane, Nikita Mankar, Shraddha Mazire, Rasika Pashankar

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Content sharing sites are very useful in sharing information and images. However, with the increasing demand of content sharing sites privacy and security concern have also increased. There is need to develop a tool for controlling user access to their shared content. Therefore, we are developing an Adaptive Privacy Policy Prediction (A3P) system which is helpful for users to create privacy settings for their images. We propose the two-level framework which assigns the best available privacy policy for the users images according to users available histories on the site.

Keywords: online information services, prediction, security and protection, web based services

Procedia PDF Downloads 280
498 Identifying Self-Disclosure in Indonesian Reality Show: A Comprehensive Study

Authors: Dwi Ashari

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This study attempts to disclose people’s privacy in Indonesian media. Many television (henceforth: TV) programs have shown the exposure of people’s privacy. People, not only celebrities, who appear in TV program often, share their life to the participants to get very intimate self-disclosure with them. Indonesia, as one of the countries with highest population, has many people who watch television everyday. This can be the major factor for some TV stations to create a program to get people’s attention to gain more profit. This study examines some factors of Indonesia TV programs that share the people’s privacy. The relation of privacy in Indonesia TV programs will be related to the concept of self-disclosure and intimacy between the people who share and watch the programs.

Keywords: Indonesia, media, privacy, self-disclosure

Procedia PDF Downloads 251
497 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

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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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496 Protection of Minor's Privacy in Bosnian Herzegovinian Media (Legal Regulation and Current Media Reporting)

Authors: Ilija Musa

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Positive legal regulation of juvenile privacy protection, current state of showing a child in BH media and possibilities of a child’s privacy protection by more adequate media legislature which should be arranged in accordance to recommendations of the UN Committee on the Rights of the Child for Bosnia and Herzegovina. Privacy of the minors in Bosnian-Herzegovinian media is insufficiently legally arranged. Due to the fact that there is no law on media area arrangement at the state level, electronic media are under jurisdiction of Communications regulatory agency, which at least partially, regulated the sector of radio and television broadcasting by adequate protection of child’s privacy. However, print and online media are under jurisdiction of non-governmental association Print and online media council in B&H which is not authorized to punish violators of this body’s Codex, what points out the necessity of passing the unique media law which would enable sanctioning the child’s privacy violation. The analysis of media content, which is a common violation of the child's privacy, analysis of positive legislation which regulates the media, confirmed the working hypothesis by which the minor’s protection policy in BH media is not protected at the appropriate level. Taking this into consideration, in the conclusion of this article the author gives recommendations for the regulation of legal protection of minor’s privacy in BH media.

Keywords: children, media, legislation, privacy protection, Bosnia Herzegovina

Procedia PDF Downloads 409
495 Offshore Outsourcing: Global Data Privacy Controls and International Compliance Issues

Authors: Michelle J. Miller

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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 329
494 An Overview of Privacy and Security Issues in Social Networks

Authors: Mohamad Ibrahim Al Ladan

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Social networks, such as Facebook, Myspace, LinkedIn, Google+, and Twitter have experienced exponential growth and a remarkable adoption rate in recent years. They provide attractive means of online social interactions and communications with family, friends, and colleagues from around the corner or across the globe, and they have become an important part of daily digital interactions for more than one and a half billion users around the world. The various personal information sharing practices that social network providers encourage have led to their success as innovative social interaction platforms. However, these practices have resulted in ample concerns with respect to privacy and security from different stakeholders. Addressing these privacy and security concerns in social networks is a must for these networks to be sustainable. Existing security and privacy tools may not be enough to address existing concerns. Some guidelines should be followed to protect users from the existing risks. In this paper, we have investigated and discussed the various privacy and security issues and concerns pertaining to social networks. Moreover, we have classified these privacy and security issues and presented a thorough discussion of the implications of these issues and concerns on the future of the social networks. In addition, we have presented a set of guidelines as precaution measures that users can consider to address these issues and concerns.

Keywords: social networks privacy issues, social networks security issues, social networks privacy precautions measures, social networks security precautions measures

Procedia PDF Downloads 186
493 Other-Generated Disclosure: A Challenge to Privacy on Social Network Sites

Authors: Tharntip Tawnie Chutikulrungsee, Oliver Kisalay Burmeister, Maumita Bhattacharya, Dragana Calic

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Sharing on social network sites (SNSs) has rapidly emerged as a new social norm and has become a global phenomenon. Billions of users reveal not only their own information (self disclosure) but also information about others (other-generated disclosure), resulting in a risk and a serious threat to either personal or informational privacy. Self-disclosure (SD) has been extensively researched in the literature, particularly regarding control of individual and existing privacy management. However, far too little attention has been paid to other-generated disclosure (OGD), especially by insiders. OGD has a strong influence on self-presentation, self-image, and electronic word of mouth (eWOM). Moreover, OGD is more credible and less likely manipulated than SD, but lacks privacy control and legal protection to some extent. This article examines OGD in depth, ranging from motivation to both online and offline impacts, based upon lived experiences from both ‘the disclosed’ and ‘the discloser’. Using purposive sampling, this phenomenological study involves an online survey and in-depth interviews. The findings report the influence of peer disclosure as well as users’ strategies to mitigate privacy issues. This article also calls attention to the challenge of OGD privacy and inadequacies in the law related to privacy protection in the digital domain.

Keywords: facebook, online privacy, other-generated disclosure, social networks sites (SNSs)

Procedia PDF Downloads 162
492 Privacy Label: An Alternative Approach to Present Privacy Policies from Online Services to the User

Authors: Diego Roberto Goncalves De Pontes, Sergio Donizetti Zorzo

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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 225