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Privacy Design Strategies
Abstract and Figures
In this paper we define the notion of a privacy design strategy. These
strategies help IT architects to support privacy by design early in the
software development life cycle, during concept development and analysis. Using
current data protection legislation as point of departure we derive the
following eight privacy design strategies: minimise, hide, separate, aggregate,
inform, control, enforce, and demonstrate. The strategies also provide a useful
classification of privacy design patterns and the underlying privacy enhancing
technologies. We therefore believe that these privacy design strategies are not
only useful when designing privacy friendly systems, but also helpful when
evaluating the privacy impact of existing IT systems.
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... Similar in some respects to earlier research into frequent itemset mining, such as PrivBasis (Li et al, 2012), this form of machine learning frequently operates over microdata streams or event-based data, such as transactional records, to optimize sales, marketing, and operational efficiencies. Minimizing data for publication is beneficial to adhere to privacy best practices (Hoepman, 2020), whether the reduction of dimensionality improves the results of a specific transform, like differential privacy. It is important to consider that practical contributions should not be overfitted for a specific domain where generalized approaches with more applicability and reusability are possible. ...
As continued data breaches allow state-level threat actors to assemble expansive dossiers on populations to carry out information warfare objectives, protecting personal privacy in published data sets and internal data stores is increasingly essential to civilian and societal safety. At the same time, the explosion of high-resolution, high-accuracy microdata streams, such as timestamped geolocation coordinates collected simultaneously by hardware platforms, operating systems, and a multitude of on-device applications and sites establishes a layered, highly-correlated pattern of life that can uniquely identify individuals and allow for targeted information warfare actions. Differential privacy (DP) is an advanced but highly effective technique in protecting sensitive data streams. This robust approach preserves privacy in published data sets through additive statistical noise sampled from Gaussian or Laplacian probability distributions. Data sets that contain highly correlated event-based data require specialized techniques to preserve mathematical DP guarantees in microdata streams beyond "user-level" applications available in most off-the-shelf approaches. Because practitioners need more tools to assess the robustness of differentially private outputs in microdata streams, application errors may result in future reidentification and privacy loss for data subjects. This research yields an artifact that can reassociate events in microdata streams when insufficient naive approaches are used. It also serves as a tool for implementers to validate their approaches in highly correlated event data.
... This analysis examines privacy-solution-based design approaches. This method adopts a privacy design strategy early in development, namely MINIMISE, HIDE, SEPARATE, AGGREGATE, INFORM, CONTROL, ENFORCE, and DEMONSTRATE [77]. Several strategies exist for preserving user data, including minimization, hiding, and separation. ...
Today, blockchain technology has emerged in healthcare as a privacy-enhanced technology as well as a supporting infrastructure to foster collaboration among healthcare stakeholders. Understanding the flow of data on the blockchain is critical to developing effective privacy policies so that recorded data neither identifies a patient nor provides sufficient detail for re-identification. This study aims to understand how blockchain can protect a patient's identity and include personally identifiable information (PII) from disclosure. In this study, we conducted a systematic literature review (SLR) of state-of-the-art blockchain and healthcare data management approaches to identify blockchain-based patient privacy solutions and present privacy protection techniques against blockchain data flow breaches. Using Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) analysis, this study identified 1245 research articles published between 2019 and 2021 from well-known sources such as IEEE Xplore, ACM Digital Library, Springs Link, Scopus, and MDPI. A total of 1223 unique articles were identified, and 58 articles were selected based on the inclusion criteria for this SLR to highlight the privacy gaps related to blockchain applied to healthcare. First, this study presents research papers on blockchain-based healthcare that address patient privacy. A categorization of methods such as pseudonymization, consensus algorithms, data structure, on-chain privacy, and hash-chain storage methods follows this. It also describes some privacy issues that arise when using blockchain in healthcare data management, such as pseudo-linkability, forgery, and leaked transactions that lead to compromised patient identity. An overview of cryptographic and machine learning techniques is then provided to address the leakage problem in the blockchain implementation context. Finally, this SLR provides multiple dimensions of privacy that should be considered to ensure holistic patient privacy, from data collection to processing, storage, analysis, and sharing.
... Thus, a well-defined Privacy Knowledge Base (PKB) is essential to support decision-making in system development and re-engineering [35]. The PKB defines which principles of PbD are violated by a specific vulnerability and which Privacy Design Strategies [36] must be adopted to mitigate it. ...
With the advent of Quantum Computing and its exponential research endeavors in the past couple of decades, we are looking at a Golden Era of Quantum Computing. We are transitioning into an age of Hybrid Classical‐Quantum Computers, where the quantum computational resources are selectively harnessed for resource‐intensive tasks. On the one hand, Quantum Computing promises immense future computational innovation, and it also comes with privacy and security challenges. To date, Privacy by Design (PbD) and Security by Design (SbD) frameworks and guidelines in the Quantum Software Engineering (QSE) domain are still nebulous, and there are no comprehensive studies on the same. In this study, therefore, we identify the current state‐of‐the‐art in the relevant literature and investigate the principles of PbD and SbD in the domain of QSE. This is the first study to identify state‐of‐the‐art Quantum PbD and Quantum SbD in QSE. Furthermore, we also identified the gaps in the current literature, which were extended into action points for a robust literature for Quantum PbD and SbD. We recognize the crucial role of researchers, academics, and professionals in the field of Quantum Computing and Software Engineering in conducting more empirical studies and shaping the future of PbD and SbD principles in QSE.
This chapter explores the integration of everyday objects with the Internet, commonly known as the Internet of things (IoT) and its transformative impact on human lives and urban environments. The chapter begins with an introduction to IoT, providing foundational knowledge about how this technology merges the physical and digital worlds through interconnected devices, ranging from home appliances to complex urban infrastructure. It emphasises the potential benefits of IoT, such as enhanced efficiency, convenience, and personalisation of user experiences, while also addressing significant concerns regarding privacy, security, and the socioeconomic implications of widespread technology deployment. A substantial focus is placed on how IoT technologies shape and are shaped by urban environments, using metropolitan projects as examples to explore the integration of technology within cities. The narrative investigates strategic considerations related to the embedding of IoT in urban landscapes, questioning how technology can harmoniously coexist with ecological and human dimensions of city life. Moreover, the chapter delves into responsive environments (RE), an area of human–technology Interaction (HTI) that merges architecture, computer science, and cognitive science to create spaces that adapt to human presence and behaviour.
Stefan Brands proposes cryptographic building blocks for the design of digital certificates that preserve privacy without sacrificing security.
As paper-based communication and transaction mechanisms are replaced by automated ones, traditional forms of security such as photographs and handwritten signatures are becoming outdated. Most security experts believe that digital certificates offer the best technology for safeguarding electronic communications. They are already widely used for authenticating and encrypting email and software, and eventually will be built into any device or piece of software that must be able to communicate securely. There is a serious problem, however, with this unavoidable trend: unless drastic measures are taken, everyone will be forced to communicate via what will be the most pervasive electronic surveillance tool ever built. There will also be abundant opportunity for misuse of digital certificates by hackers, unscrupulous employees, government agencies, financial institutions, insurance companies, and so on.In this book Stefan Brands proposes cryptographic building blocks for the design of digital certificates that preserve privacy without sacrificing security. Such certificates function in much the same way as cinema tickets or subway tokens: anyone can establish their validity and the data they specify, but no more than that. Furthermore, different actions by the same person cannot be linked. Certificate holders have control over what information is disclosed, and to whom. Subsets of the proposed cryptographic building blocks can be used in combination, allowing a cookbook approach to the design of public key infrastructures. Potential applications include electronic cash, electronic postage, digital rights management, pseudonyms for online chat rooms, health care information storage, electronic voting, and even electronic gambling.
Consider a data holder, such as a hospital or a bank, that has a privately held collection of person-specific, field structured data. Suppose the data holder wants to share a version of the data with researchers. How can a data holder release a version of its private data with scientific guarantees that the individuals who are the subjects of the data cannot be re-identified while the data remain practically useful? The solution provided in this paper includes a formal protection model named k-anonymity and a set of accompanying policies for deployment. A release provides k-anonymity protection if the information for each person contained in the release cannot be distinguished from at least k-1 individuals whose information also appears in the release. This paper also examines re-identification attacks that can be realized on releases that adhere to k-anonymity unless accompanying policies are respected. The k-anonymity protection model is important because it forms the basis on which the real-world systems known as Datafly, μ-Argus and k-Similar provide guarantees of privacy protection.
SUMMARYA privacy enhancing technology (PET) is an application or a mechanism that allows users to protect the privacy of their personally identifiable information. Early PETs were about enabling anonymous mailing and anonymous browsing, but lately, there have been active research and development efforts in many other problem domains. This paper describes the first pattern language for developing PETs. Currently, it contains 12 patterns. These privacy patterns are not limited to a specific problem domain; they can be applied to design anonymity systems for various types of online communication, online data sharing, location monitoring, voting, and electronic cash management. The pattern language guides a developer when he or she is designing a PET for an existing problem or innovating a solution for a new problem. Copyright © 2011 John Wiley & Sons, Ltd.
Individuals complain to the Information and Privacy Commissioner (IPC) when they believe an institution has breached their privacy. In this issue of IPC Practices, the IPC offers institutions practical suggestions to assist them in responding to the IPC during privacy investigations, and to prevent common breaches of privacy under the Freedom of Information and Protection of Privacy Act (the provincial Act) and the Municipal Freedom of Information and Protection of Privacy Act (the municipal Act). The IPC also suggests that Freedom of Information and Privacy Co-ordinators ensure that all employees who deal with the issues listed below are aware of the relevant tips. Medical Certificates When employees are absent from work, institutions often request a letter or medical certificate issued by the employee's doctor explaining the absence. The IPC has received complaints about this collec- tion of personal information. Investigations have revealed that the manner of collection was not in compliance with the Acts. Institutions sometimes contacted the doctor directly to request the certifi- cate without the employee's knowledge or consent. The IPC found that indirect collection of personal information in this circumstance was not in compliance with the Acts.