Enforcement Of Privacy Policies Research Articles

Protection of confidential data in Amazon Elastic Block Store snapshots

The article examines security issues in an Amazon Web Services (AWS) environment with an emphasis on the risks associated with public Elastic Block Store (EBS) snapshots. Analyzes how user identification and authorization processes can contribute to security risks and leakage of confidential data. Methods for detecting and monitoring public snapshots are described in detail, as well as recommendations for changing permissions and ensuring the security of credentials. Emphasizes the importance of conducting investigations when public snapshots are discovered, and develops strategies to prevent such incidents, including staff training and enforcement of privacy policies. AWS's approach to security is highlighted, particularly through informing customers about the potential risks associated with public access to snapshots. The paper offers a comprehensive overview of security issues in an AWS environment, focusing on data management, and recommends specific measures to ensure the secure use of EBS snapshots. Potential vulnerabilities are illustrated with real-world examples, demonstrating the importance of properly managing access policies and the informed use of snapshots to prevent accidental data leakage. Particular attention is paid to the impact of open snapshots on various industries and the need for audits to identify potential threats.

Medical Boards: Impact of Growing Virtual Care and Need for Integrated Approach to Enhance Quality and Safety

The increased amount of virtual care during the COVID-19 pandemic has exacerbated the challenge of providing appropriate medical board oversight to ensure proper quality of care delivery and safety of patients. This is partly due to the conventional model of each state medical board (SMB) holding responsibility for medical standards and oversight only within the jurisdiction of that state board and partly due to regulatory waivers and reduced enforcement of privacy policies. Even with a revoked license in one state, significant number of physicians have continued to practice by obtaining a medical license in a different state. Individualized requests were sent to 63 medical boards with questions related to practice of telemedicine and digital health by debarred or penalized medical doctors. The responses revealed major deficiencies and the urgent need to adopt a nationwide framework and to create an anchor point to serve as the coordinator of all relevant information related to incidents of improper medical practice. The ability to cause damage to large number of patients is significantly more now. Federal and state agencies urgently need to provide more attention and funding to issues related to quality of care and patient care in the changing ecosystem that includes medical specialists at a distance and the use of evolving digital health services and products. The creation, maintenance, and use of an integrated information system at national and multinational levels is increasingly important.

The Layered Privacy Language Art. 12 – 14 GDPR Extension – Privacy Enhancing User Interfaces

Since 25th May 2018, the EU-wide General Data Protection Regulation (GDPR) applies in order to strengthen the rights of Data Subjects. Although the GDPR specifies the required information, which has to be presented to a Data Subject, it can still be argued for a lack of transparency due to unfavourable presentation of the privacy policy. Furthermore, no systematic approach for the enforcement of privacy policies in technical systems is deployed. These issues are tackled by the both human- and machine-readable Layered Privacy Language (LPL), which models legal privacy policies. This work introduces an extension for LPL to comply with Art. 12 – 14 GDPR. Additionally, user interface prototypes will be introduced to allow the creation of LPL privacy policies by the Data Protection Officer as well as a structured presentation of the LPL privacy policy for web-applications.

A Qualitative Analysis of Information Privacy Concerns among Healthcare Employees

Healthcare organizationsprocess massive amount of electronic medical records (EMR) utilizedby their employees. Having given privileged access to sensitive and valuable patient information in the EMR, healthcare employees may cause privacy breaches, which may lead to detrimental consequences. Therefore, it is vital to impose particular attention to healthcare employees’ concerns on privacy in the use of EMR. The purpose of this paper is to indicate the results from quantitative analysis (phase 1) through qualitative analysis (phase 2). From phase 1, privacy awareness is the only factor that influences information privacy concerns (IPC) in the use of EMRamong healthcare employees, suggesting that healthcare employees do not think that privacy policy, privacy control, privacy risk, and privacy experience influence their IPC. To investigate why the respondents answered in this manner, the interviewees were asked to clarify why they think privacy awareness is veryimportant to ensure the IPC among healthcare employees. Indeed, the mediating relationship of privacy awareness between privacy policy and IPC were discussed by highlighting on the implementation practices regarding privacy policy enforcement and awareness programs in healthcare organization.

Data provenance to audit compliance with privacy policy in the Internet of Things

Managing privacy in the IoT presents a significant challenge. We make the case that information obtained by auditing the flows of data can assist in demonstrating that the systems handling personal data satisfy regulatory and user requirements. Thus, components handling personal data should be audited to demonstrate that their actions comply with all such policies and requirements. A valuable side-effect of this approach is that such an auditing process will highlight areas where technical enforcement has been incompletely or incorrectly specified. There is a clear role for technical assistance in aligning privacy policy enforcement mechanisms with data protection regulations. The first step necessary in producing technology to accomplish this alignment is to gather evidence of data flows. We describe our work producing, representing and querying audit data and discuss outstanding challenges.

Privacy and data protection: Legal aspects in the Republic of Macedonia

The purpose of this paper is to present a theoretical assessment of the existing Law on Personal Data Protection in the Republic of Macedonia. The paper aims to analyse whether there is a need for additional legal tools in order to achieve a balance between maintaining data integrity in the digital age and the use of modern technology. The paper discusses the meaning of “information privacy” in the age of big data, cyber threats and the domestic and international response to these issues. Special focus is dedicated to privacy policy enforcement in European Union Law. Having regard to the development of new technologies, prevailing data protection legislation may no longer be able to provide effective protection for individuals’ personal information. Therefore, existing laws should be continuously adapted to respond to new challenges and situations deriving from different online activities and communications.

A Framework for Composition and Enforcement of Privacy-Aware and Context-Driven Authorization Mechanism for Multimedia Big Data

The proliferation of multimedia big data for dissemination and sharing of massive amounts of information raises important security and privacy concerns. One such concern is the composition and enforcement of privacy policies in order to securely manage access of multimedia big data. Several researchers have pointed out that for proper enforcement of privacy policies, the privacy requirements should be captured in access control systems. In this paper, we propose a hybrid approach where privacy requirements are captured in an access control system and present a framework for composition and enforcement of privacy policies. The focus is to allow a user, not a system or security administrator to compose conflict free policies for their online multimedia data. An additional requirement is that such a policy be context-aware. We also present a methodology for verifying the privacy policy in order to ensure correctness and logical consistency. The verification process is also used to ensure that sensitive security requirements are not violated when privacy rules are enforced. A prototype, named Intelligent Privacy Manager (iPM), has been implemented for sharing of multimedia big data in a secure and private manner.

Security, privacy and trust in Internet of Things: The road ahead

Internet of Things (IoT) is characterized by heterogeneous technologies, which concur to the provisioning of innovative services in various application domains. In this scenario, the satisfaction of security and privacy requirements plays a fundamental role. Such requirements include data confidentiality and authentication, access control within the IoT network, privacy and trust among users and things, and the enforcement of security and privacy policies. Traditional security countermeasures cannot be directly applied to IoT technologies due to the different standards and communication stacks involved. Moreover, the high number of interconnected devices arises scalability issues; therefore a flexible infrastructure is needed able to deal with security threats in such a dynamic environment. In this survey we present the main research challenges and the existing solutions in the field of IoT security, identifying open issues, and suggesting some hints for future research.

A novel policy-driven reversible anonymisation scheme for XML-based services

This paper proposes a reversible anonymisation scheme for XML messages that supports fine-grained enforcement of XACML-based privacy policies. Reversible anonymisation means that information in XML messages is anonymised, however the information required to reverse the anonymisation is cryptographically protected in the messages. The policy can control access down to octet ranges of individual elements or attributes in XML messages. The reversible anonymisation protocol effectively implements a multi-level privacy and security based approach, so that only authorised stakeholders can disclose confidential information up to the privacy or security level they are authorised for. The approach furthermore supports a shared secret based scheme, where stakeholders need to agree to disclose confidential information. Last, it supports time limited access to private or confidential information. This opens up for improved control of access to private or confidential information in XML messages used by a service oriented architecture. The solution provides horizontally scalable confidentiality protection for certain types of big data applications, like XML databases, secure logging and data retention repositories.

The FTC and the New Common Law of Privacy

One of the great ironies about information privacy law is that the primary regulation of privacy in the United States has barely been studied in a scholarly way. Since the late 1990s, the Federal Trade Commission (FTC) has been enforcing companies’ privacy policies through its authority to police unfair and deceptive trade practices. Despite over fifteen years of FTC enforcement, there is no meaningful body of judicial decisions to show for it. The cases have nearly all resulted in settlement agreements. Nevertheless, companies look to these agreements to guide their privacy practices. Thus, in practice, FTC privacy jurisprudence has become the broadest and most influential regulating force on information privacy in the United States — more so than nearly any privacy statute or any common law tort. In this Article, we contend that the FTC’s privacy jurisprudence is functionally equivalent to a body of common law, and we examine it as such. We explore how and why the FTC, and not contract law, came to dominate the enforcement of privacy policies. A common view of the FTC’s privacy jurisprudence is that it is thin, merely focusing on enforcing privacy promises. In contrast, a deeper look at the principles that emerge from FTC privacy “common law” demonstrates that the FTC’s privacy jurisprudence is quite thick. The FTC has codified certain norms and best practices and has developed some baseline privacy protections. Standards have become so specific they resemble rules. We contend that the foundations exist to develop this “common law” into a robust privacy regulatory regime, one that focuses on consumer expectations of privacy, extends far beyond privacy policies, and involves a full suite of substantive rules that exist independently from a company’s privacy representations.

A privacy preserving authorisation system for the cloud

In this paper we describe a policy based authorisation infrastructure that a cloud provider can run as an infrastructure service for its users. It will protect the privacy of usersʼ data by allowing the users to set their own privacy policies, and then enforcing them so that no unauthorised access is allowed to their data. The infrastructure ensures that the usersʼ privacy policies are stuck to their data, so that access will always be controlled by the policies even if the data is transferred between cloud providers or services. This infrastructure also ensures the enforcement of privacy policies which may be written in different policy languages by multiple authorities such as: legal, data subject, data issuer and data controller. A conflict resolution strategy is presented which resolves conflicts among the decisions returned by the different policy decision points (PDPs). The performance figures are presented which show that the system performs well and that each additional PDP only imposes a small overhead.

A general framework for managing and processing live video data with privacy protection

Though a large body of existing work on video surveillance focuses on image and video processing techniques, few address the usability of such systems, and in particular privacy issues. This study fuses concepts from stream processing and content-based image retrieval to construct a privacy-preserving framework for rapid development and deployment of video surveillance applications. Privacy policies, instantiated to as privacy filters, may be applied both granularly and hierarchically. Privacy filters are granular as they are applicable to specific objects appearing in the video streams. They are hierarchal because they can be specified at specific objects in the framework (e.g., users, cameras) and are combined such that the disseminated video stream adheres to the most stringent aspect specified in the cascade of all privacy filters relevant to a video stream or query. To support this privacy framework, we extend our Live Video Database Model with an informatics-based approach to object recognition and tracking and add an intrinsic privacy model that provides a level of privacy protection not previously available for real-time streaming video data. The proposed framework also provides a formal approach to implement and enforce privacy policies that are verifiable, an important step towards privacy certification of video surveillance systems through a standardized privacy specification language.

An approach to domain-based scalable context management architecture in pervasive environments

In pervasive environments, context management systems are expected to administrate large volume of contextual information that is captured from spatial to nonspatial elements. Research in context-aware computing produced a number of middleware systems for context management to intermediate the communications between applications and context providers. In particular, in pervasive environments, the design of distributed storage, retrieval and propagation mechanisms of context information across domains is vital. In this paper, we propose a domain-based approach to address the requirements of scalable distributed context management, cross-domain efficient context information dissemination and domain-based privacy policy enforcement. We propose infinitum, a middleware architecture that incorporates the management and communication benefits of the Google Wave Federation Protocol, while also taking advantage of the semantic and inference benefits of ontology-based context models. This architecture establishes a robust cross-domain scalable context management and collaboration framework, which has been implemented and evaluated in a real-life application of "SMART University" to support virtual team collaboration.

Privacy-compliant Disclosure of Personal Data to Third Parties

Abstract Privacy in cloud computing is at the moment simply a promise to be kept by the software service providers. Users are neither able to control the disclosure of personal data to third parties nor to check if the software service providers have followed the agreed-upon privacy policy. Therefore, disclosure of the users´ data to the software service providers of the cloud raises privacy risks. This project focuses on enhancing the trust model of the practice so that users and auditors, e. g., data protection officers, can check whether software service providers have followed the agreed-upon privacy policy for a disclosure of personal data to third parties. As a countermeasure by an ex post enforcement of privacy policies, we propose to observe disclosures of personal data to third parties by using data provenance history and digital watermarking.

Privacy policies for shared content in social network sites

Social networking is one of the major technological phenomena of the Web 2.0, with hundreds of millions of subscribed users. Social networks enable a form of self-expression for users and help them to socialize and share content with other users. In spite of the fact that content sharing represents one of the prominent features of existing Social network sites, they do not provide any mechanisms for collective management of privacy settings for shared content. In this paper, using game theory, we model the problem of collective enforcement of privacy policies on shared data. In particular, we propose a solution that offers automated ways to share images based on an extended notion of content ownership. Building upon the Clarke-Tax mechanism, we describe a simple mechanism that promotes truthfulness and that rewards users who promote co-ownership. Our approach enables social network users to compose friendship based policies based on distances from an agreed upon central user selected using several social networks metrics. We integrate our design with inference techniques that free the users from the burden of manually selecting privacy preferences for each picture. To the best of our knowledge, this is the first time such a privacy protection mechanism for social networking has been proposed. We also extend our mechanism so as to support collective enforcement across multiple social network sites. In the paper, we also show a proof-of-concept application, which we implemented in the context of Facebook, one of today's most popular social networks. Through our implementation, we show the feasibility of such approach and show that it can be implemented with a minimal increase in overhead to end-users. We complete our analysis by conducting a user study to investigate users' understanding of co-ownership, usefulness and understanding of our approach. Users responded favorably to the approach, indicating a general understanding of co-ownership and the auction, and found the approach to be both useful and fair.

Regulatory Compliance and the Correlation to Privacy Protection in Healthcare

Recent government-led efforts and industry-sponsored privacy initiatives in the healthcare sector have received heightened publicity. The current set of privacy legislation mandates that all parties involved in the delivery of care specify and publish privacy policies regarding the use and disclosure of personal health information. The authors’ study of actual healthcare privacy policies indicates that the vague representations in published privacy policies are not strongly correlated with adequate privacy protection for the patient. This phenomenon is not due to a lack of available technology to enforce privacy policies, but rather to the will of the healthcare entities to enforce strong privacy protections and their interpretation of minimum compliance obligations. Using available information systems and data mining techniques, this article describes an infrastructure for privacy protection based on the idea of policy refinement to allow the transition from the current state of perceived to be privacy-preserving systems to actually privacy-preserving systems.

An open source toolkit for medical imaging de-identification

Medical imaging acquired for clinical purposes can have several legitimate secondary uses in research projects and teaching libraries. No commonly accepted solution for anonymising these images exists because the amount of personal data that should be preserved varies case by case. Our objective is to provide a flexible mechanism for anonymising Digital Imaging and Communications in Medicine (DICOM) data that meets the requirements for deployment in multicentre trials. We reviewed our current de-identification practices and defined the relevant use cases to extract the requirements for the de-identification process. We then used these requirements in the design and implementation of the toolkit. Finally, we tested the toolkit taking as a reference those requirements, including a multicentre deployment. The toolkit successfully anonymised DICOM data from various sources. Furthermore, it was shown that it could forward anonymous data to remote destinations, remove burned-in annotations, and add tracking information to the header. The toolkit also implements the DICOM standard confidentiality mechanism. A DICOM de-identification toolkit that facilitates the enforcement of privacy policies was developed. It is highly extensible, provides the necessary flexibility to account for different de-identification requirements and has a low adoption barrier for new users.

Privacy policy enforcement in enterprises with identity management solutions

People are usually asked by enterprises to disclose their personal information to access web services and engage in business interactions. Enterprises need this information to enable their business...

Privacy policy enforcement in enterprises with identity management solutions

People are usually asked by enterprises to disclose their personal information to access web services and engage in business interactions. Enterprises need this information to enable their business processes. This is unlikely to change, at least in the foreseeable future. When collecting personal data, enterprises must satisfy privacy laws and policies along with addressing people's expectations on how their data should be handled. Currently much is done by means of manual processes, in particular in terms of privacy enforcement: these processes are prone to mistakes and hard to comply with. Automation can help enterprises to deal with these privacy management issues, in particular the enforcement of privacy policies on collected personal data. Enterprises have already been investing in identity management solutions: they require that approaches to automate privacy management should keep into account and leverage these solutions. This paper discusses our research and development work to automate the enforcement of privacy policies in enterprises. Our model of privacy policy enforcement is introduced along with the technical details of a related prototype, integrated (as a proof of concept) with HP Select Access, a state-of-the-art identity management solution. This technology is currently under productisation. We discuss our current results and next steps.

Service-Oriented Distributed Data Mining

Data mining research currently faces two great challenges: how to embrace data mining services with just-in-time and autonomous properties and how to mine distributed and privacy-protected data. To address these problems, the authors adopt the Business Process Execution Language for Web Services in a service oriented distributed data mining (DDM) platform to choreograph DDM component services and fulfill global data mining requirements. They also use the learning-from-abstraction methodology to achieve privacy-preserving DDM. Finally,they illustrate how localized autonomy on privacy-policy enforcement plusa bidding process can help the service-oriented system self-organize.