scholarly journals Privacy Protection and Data Breaches

10.28945/2261 ◽  
2015 ◽  
Author(s):  
S. Srinivasan

Data breach is the act of accessing a central data repository without the consent of the data owner. Data breaches are occurring frequently and involve millions of records. Major breaches have been reported since 2005. Often data breaches occur due to someone with malicious intent accessing the stored data. In this paper we look at the types of data breaches and how they impact people’s privacy, we introduce a data protection model with the goal of protecting people’s privacy. Given today’s mobile information needs it is essential to have access to personal data. Social networks are making it difficult to keep personal information private. We provide several different summaries to show the effect of data breaches and data losses on people. We conclude this paper with a set of recommendations to protect people’s privacy.

Author(s):  
Ronggong Song ◽  
Larry Korba ◽  
George Yee

Pseudonym technology is attracting more and more attention and, together with privacy violations, is becoming a major issue in various e-services. Current e-service systems make personal data collection very easy and efficient through integration, interconnection, and data mining technologies since they use the user’s real identity. Pseudonym technology with unlinkability, anonymity, and accountability can give the user the ability to control the collection, retention, and distribution of his or her personal information. This chapter explores the challenges, issues, and solutions associated with pseudonym technology for privacy protection in e-services. To have a better understanding of how the pseudonym technology provides privacy protection in e-services, we describe a general pseudonym system architecture, discuss its relationships with other privacy technologies, and summarize its requirements. Based on the requirements, we review, analyze, and compare a number of existing pseudonym technologies. We then give an example of a pseudonym practice — e-wallet for e-services and discuss current issues.


Author(s):  
R R. Arnesen

Protecting the privacy of citizens is a critical issue in digital government services. The right to privacy is widely recognized as a fundamental human right, as stated in Article 12 of the Universal Declaration of Human Rights (United Nations, 1948). The first definition of privacy was given by American lawyers Warren and Brandeis (1890), who defined it as “the right to be let alone.” However, the right to privacy has been recognized for millenniums. The Hippocratic oath (n.d.) dates back to around 400 B.C. and instructs medical doctors to respect the privacy of their patients. During the last three decades, many countries have passed privacy legislation, the Swedish Data Act from 1973 being the first national privacy act in the world. During the 1970s, many countries adopted data protection acts (Fischer-Hübner, 2001). In 1980, OECD published its privacy guidelines with the purpose of reducing the potential privacy problems incurred by cross-border trade (OECD, 1980). The European Council adopted Directive 95/46/EC in 1995, and all member states are required to implement national privacy legislation in compliance with this directive (European Union (EU) Directive 95/46/EC, 1995). Privacy is under increasing pressure in the digital age, and the introduction of digital government services may escalate this development. The way government has been organized until now, with separate departments with their own “silos” of personal data, has inherently provided some privacy protection. In such a distributed environment data matching is expensive and resource consuming. This form of privacy protection is referred to as “practical obscurity” in Crompton (2004, p.12). Some examples of threats to privacy related to the development of digital government are as follows: • Data collection capabilities increase as new technology for continuous and automatic data collection is introduced. Examples of such technologies include digital video surveillance, biometric identification and radio frequency identification (RFID). • Data processing capabilities are rapidly increasing. The very existence of large amounts of stored personal data, together with the availability of sophisticated tools for analysis, increases the probability for misuse of data. • There is a trend towards integration of formerly separated governmental services, including physical offices. Providing a single point of contact is more user friendly, but it may also provide an attacker with a single point of attack. • Outsourcing of services (e.g., customer relationship management) is increasingly popular both among companies and governmental organizations. Those who deliver such services to many customers have a unique opportunity to gather personal information from many different sources. If services are outsourced across country borders, and perhaps in several layers, responsibilities soon become unclear. • Even if the organization responsible for stored personal information does not have malicious intents, one cannot expect all its employees to be equally trustworthy. Disloyal employees are a severe threat when increasing amounts of information are stored. • Tax records and other public records made available on the Internet enable efficient searches and aggregation of information about individuals. Identity thefts and fraud are common uses of information gathered in this way.


2021 ◽  
pp. 1-27
Author(s):  
Viola Ackfeld ◽  
Tobias Rohloff ◽  
Sylvi Rzepka

Abstract Personal data increasingly serve as inputs to public goods. Like other types of contributions to public goods, personal data are likely to be underprovided. We investigate whether classical remedies to underprovision are also applicable to personal data and whether the privacy-sensitive nature of personal data must be additionally accounted for. In a randomized field experiment on a public online education platform, we prompt users to complete their profiles with personal information. Compared to a control message, we find that making public benefits salient increases the number of personal data contributions significantly. This effect is even stronger when additionally emphasizing privacy protection, especially for sensitive information. Our results further suggest that emphasis on both public benefits and privacy protection attracts personal data from a more diverse set of contributors.


Author(s):  
Iwan Erar Joesoef ◽  

Information technology-based money lending service is the implementation of financial services to bring lenders together with borrowers in order to make loan agreements to borrow money. Many people have complained about the dissemination of personal data by online loan providers without notice and without the owner's permission. The purpose of this paper is to review the legal protection of customers' personal data in technology-based money lending services. The motto used in this writing is a normative legal method with a statutory approach and a fact approach. The results of the study show that legal protections and sanctions for personal data breaches have been stipulated in Law No. 11 of 2008 and changes in information and electronic transactions, but specifically regarding legal protection and sanctions for personal data breaches in online loan services have been listed in the financial services authority regulation No. 77/POJK.01/2016. Information technology-based money lending services. The organizer is responsible for maintaining the confidentiality, integrity, and availability of personal data use and in the utilization must obtain approval from the data owner, the organizer may be subject to administrative sanctions in the form of written warnings, fines, obligation to pay a certain amount of money, restrictions on business activities, and revocation of business license


Author(s):  
Radi Petrov Romansky ◽  
Irina Stancheva Noninska

The contemporary digital world based on network communications, globalization and information sharing outlines new important targets in the area of privacy and personal data protection which reflect to applied principles of secure access to proposed information structures. In this reason the aim of secure access to all resources of an e-learning environment is very important and adequate technological and organizational measures for authentication, authorization and protection of personal data must be applied. Strong security procedures should be proposed to protect user's profiles, designed after successful registration and all personal information collected by educational processes. The goal of this article is to present an idea to combine traditional e-learning technologies with new opportunities that give mobile applications, cloud services and social computing. These technologies can endanger data security since they make possible remote access to resources, sharing information between participants by network communications. In order to avoid data vulnerabilities users must be identified and authenticated before, i.e. to be allowed to access information resources otherwise integrity and confidentiality of e-learning system could be destroyed. In order to propose solution basic principles of information security and privacy protection in e-learning processes are discussed in this article. As a result, an organizational scheme of a system for information security and privacy is proposed. Based on these principles a graph formalization of access to the system resources is made and architecture for combined (heterogenic) e-learning architecture with secure access to the resources is designed. Analytical investigation based on designed Markov chain has been carried out and several statistical assessments delivered by Develve software are discussed.


A breach of data is a reported occurrence where private, sensitive, or covered records have been compromised and/or released unlawfully mostly due to cyber attacks or theft. Breach of data can include personal health records, personal information, travel information, trade secrets, intellectual property, or information you provided to or is stored on a platform. Data revealed to breaches pose a security and privacy risk to Users around the world. Despite these, guidelines on how organizations can react to breaches, or how to manage information securely once it has leaked, still haveto be established. More than 3 billion people suffered and became victims of data breaches and cyber attacks in the last two decades leading to loss of personal data as well as monetary loss. This research paper conducts real time research about awareness of data privacy, kind of data/information that needs to be protected, basic protocols for staying safe online, and some of the biggest corporate data breaches that happened in this century. We bring people from different cities of India in this study through a survey and use the data provided by these 150 participants to examine their understanding of data privacy, their concern regarding their online data and the practices they follow in their daily life to keep their online data safe in this age of computers and internet.


2021 ◽  
Vol 2021 (2) ◽  
pp. 88-110
Author(s):  
Duc Bui ◽  
Kang G. Shin ◽  
Jong-Min Choi ◽  
Junbum Shin

Abstract Privacy policies are documents required by law and regulations that notify users of the collection, use, and sharing of their personal information on services or applications. While the extraction of personal data objects and their usage thereon is one of the fundamental steps in their automated analysis, it remains challenging due to the complex policy statements written in legal (vague) language. Prior work is limited by small/generated datasets and manually created rules. We formulate the extraction of fine-grained personal data phrases and the corresponding data collection or sharing practices as a sequence-labeling problem that can be solved by an entity-recognition model. We create a large dataset with 4.1k sentences (97k tokens) and 2.6k annotated fine-grained data practices from 30 real-world privacy policies to train and evaluate neural networks. We present a fully automated system, called PI-Extract, which accurately extracts privacy practices by a neural model and outperforms, by a large margin, strong rule-based baselines. We conduct a user study on the effects of data practice annotation which highlights and describes the data practices extracted by PI-Extract to help users better understand privacy-policy documents. Our experimental evaluation results show that the annotation significantly improves the users’ reading comprehension of policy texts, as indicated by a 26.6% increase in the average total reading score.


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