Privacy-enhancing Technologies Research Articles

Digital transitions in healthcare: the need for transdisciplinary research to overcome barriers of privacy enhancing technologies uptake

Abstract Purpose Adopting Privacy Enhancing Technologies (PETs) is key to accelerating digitisation of the healthcare sector while simultaneously upholding data protection rights and increasing cybersecurity. While such technologies are market-ready, the uptake of PETs in healthcare is lagging behind. In this study we explore how conflicting logics and disciplinary disparities impact PET adoption, and how transdisciplinary (TD) methods can facilitate collaboration and mutual learning to overcome these hurdles. Methods A sequential mixed-methods case analysis is employed, focusing on a specific multidisciplinary partnership within the healthcare sector. TD methods are used to examine challenges and underlying value systems related to PET adoption. Results Conflicting institutional logics in healthcare, driven by missions and value systems, hinder collaborative efforts. TD methods promote awareness of disciplinary disparities, fostering an appreciative attitude toward diverse viewpoints, with an active facilitator playing a crucial role. Mutual learning aids in identifying collective actions to address challenges but may lead to tensions when issues are framed differently. TD methods, however, fall short in guiding decision-making when preferences diverge. Conclusions Sustainable PET adoption in healthcare requires addressing underlying value systems, effective communication, interdisciplinary consciousness and mutual learning. Acknowledging and managing tensions, particularly in diverse data governance contexts, proves important for successful PET adoption. Our research highlights the complexity of multidisciplinary partnerships, urging additional research to guide decision-making and governmental interventions. Ultimately, understanding these dynamics is a constructive approach for improving healthcare practices and outcomes through PET adoption.

An Overview of Privacy-Enhancing Technologies in Biometric Recognition

Privacy-enhancing technologies are technologies that implement fundamental data protection principles. With respect to biometric recognition, different types of privacy-enhancing technologies have been introduced for protecting stored biometric data which are generally classified as sensitive. In this regard, various taxonomies and conceptual categorizations have been proposed and standardisation activities have been carried out. However, these efforts have mainly been devoted to certain sub-categories of privacy-enhancing technologies and therefore lack generalization. This work provides an overview of concepts of privacy-enhancing technologies for biometric recognition in a unified framework. Key properties and differences between existing concepts are highlighted in detail at each processing step. Fundamental characteristics and limitations of existing technologies are discussed and related to data protection techniques and principles. Moreover, scenarios and methods for the assessment of privacy-enhancing technologies for biometric recognition are presented. This paper is meant as a point of entry to the field of data protection for biometric recognition applications and is directed towards experienced researchers as well as non-experts.

Personalized and privacy-preserving federated graph neural network

High-performance GNN obtains dependencies within a graph by capturing the mechanism of message passing and aggregation between neighboring nodes in the graph, and successfully updates node embeddings. However, in practical applications, the inherent model structure of the graph is highly susceptible to privacy attacks, and the heterogeneity of external data can lead to a decrease in model performance. Motivated by this challenge, this work proposes a novel framework called Personalized Federated Graph Neural Network for Privacy-Preserving (PFGNN). Specifically, firstly, this work introduces a graph similarity strategy. Based on the principle that clients with similar features exhibit stronger homophily, this work divides all participating clients into multiple clusters for collaborative training. Furthermore, within each group, this work employs an attention mechanism to design a federated aggregation weighting scheme. This scheme is used to construct a global model on the server, which helps mitigate the difficulty of model generalization resulting from data heterogeneity collected from different clients. Lastly, to ensure the privacy of model parameters during the training process and prevent malicious adversaries from stealing them, this work implements privacy-enhancing technology by introducing an optimized function-hiding multi-input function encryption scheme. This ensures the security of both model data and user privacy. Experiments on real datasets show that our scheme outperforms FedAvg in accuracy, and the communication overhead is linearly related to the number of clients. Through this framework, PFGNN can handle all kinds of non-Euclidean structured data, multiple clients collaborate to train high-quality and highly secure global models. This work provides the foundation for designing efficient and privacy-preserving personalized federated graph neural networks.

A Comprehensive Survey of Security Challenges and Solutions of Industrial Internet of Things (IIoT) Networks

Despite the transformative potential of the Industrial Internet of Things (IIoT) in optimizing industrial operations, significant security and privacy challenges persist, underscoring the need for comprehensive solutions. This paper addresses this research gap by presenting a thorough survey of the security challenges and solutions in IIoT networks. Drawing from the extensive literature on IIoT, we identify and categorize key threats such as malware attacks, data breaches, and supply chain vulnerabilities. Additionally, we explore privacy concerns arising from the vast amounts of sensitive data collected and transmitted by IIoT devices. This analysis highlights the critical importance of encryption mechanisms, access controls, and authentication protocols as fundamental security measures. By synthesizing current research, this paper provides insights to guide the development of robust security strategies for IIoT deployments. Addressing these challenges is crucial to maintaining the reliability and integrity of industrial operations, and safeguarding against production disruptions, financial losses, and safety hazards. Through robust security measures and privacy-enhancing technologies, IIoT deployments can foster trust, compliance, and innovation in industrial settings, driving the evolution towards smart factories and connected enterprises. Keywords – Industrial Internet of Things, Cyber Security, Network Security, Threats.

On Being an Expert: Habitus as a Lens for Understanding Privacy Expertise

How do privacy experts and lay persons differ? We investigate this question using data about the use of privacy-enhancing technologies and strategies from 128 surveys and 17 follow-up interviews with two populations: privacy experts (i.e., privacy researchers and professionals) and privacy laypersons. Findings reveal that both experts and laypersons use common privacy strategies, but experts employ a broader range of strategies, favor open source technologies, and possess a more technical understanding of internet privacy risks and technologies such as onion routing and tracking algorithms. We characterize these differences in terms of sociological habitus, where experts and laypersons differ not only in technical skill but also in broader conceptualizations and practices. From this characterization, we make recommendations for technology design practices, as well as legal and pedagogical implications, that can help decenter the experiences of privacy experts and accommodate privacy laypersons' preferences and habits.

De-identification and anonymization: legal and technical approaches

"This study analyzes legal and technical approaches to data de-identification and anonymization, motivated by the need to develop balanced standards that preserve privacy without stifling beneficial data uses. Doctrinal and technical literature review methods examine provisions in major data protection laws worldwide, including the EU's GDPR, US HIPAA, and emerging frameworks in China, India, and Uzbekistan, alongside mathematical models like differential privacy and k-anonymity. The legal analysis reveals common themes like flexible research exemptions for anonymized data and calibrating standards based on sensitivity, but also gaps such as ambiguities around pseudonymization. The technical review highlights the strengths and weaknesses of encryption, perturbation, generalization, and federation techniques, emphasizing the need to complement mathematical methods with governance controls. Key findings include the importance of allowing contextual optimization, providing detailed regulatory guidance, and addressing re-identification incentives. Recommendations are provided for advancing Uzbekistan's data protection laws and practices based on international experiences, such as enabling public oversight, conducting localized impact assessments, and promoting privacy-enhancing technologies. The study concludes that to anonymize data in a way that enables research while also protecting people's rights, we need a comprehensive approach that includes laws, organizational rules, technical safeguards, ethical decision-making, and public input. All of these parts working together is important for successful data anonymization."

Privacy-Preserving Federated Survival Support Vector Machines for Cross-Institutional Time-To-Event Analysis: Algorithm Development and Validation

Background Central collection of distributed medical patient data is problematic due to strict privacy regulations. Especially in clinical environments, such as clinical time-to-event studies, large sample sizes are critical but usually not available at a single institution. It has been shown recently that federated learning, combined with privacy-enhancing technologies, is an excellent and privacy-preserving alternative to data sharing. Objective This study aims to develop and validate a privacy-preserving, federated survival support vector machine (SVM) and make it accessible for researchers to perform cross-institutional time-to-event analyses. Methods We extended the survival SVM algorithm to be applicable in federated environments. We further implemented it as a FeatureCloud app, enabling it to run in the federated infrastructure provided by the FeatureCloud platform. Finally, we evaluated our algorithm on 3 benchmark data sets, a large sample size synthetic data set, and a real-world microbiome data set and compared the results to the corresponding central method. Results Our federated survival SVM produces highly similar results to the centralized model on all data sets. The maximal difference between the model weights of the central model and the federated model was only 0.001, and the mean difference over all data sets was 0.0002. We further show that by including more data in the analysis through federated learning, predictions are more accurate even in the presence of site-dependent batch effects. Conclusions The federated survival SVM extends the palette of federated time-to-event analysis methods by a robust machine learning approach. To our knowledge, the implemented FeatureCloud app is the first publicly available implementation of a federated survival SVM, is freely accessible for all kinds of researchers, and can be directly used within the FeatureCloud platform.

Enhancing Data Privacy in Wireless Sensor Networks: Investigating Techniques and Protocols to Protect Privacy of Data Transmitted Over Wireless Sensor Networks in Critical Applications of Healthcare and National Security

The article discusses the emergence of Wireless Sensor Networks (WSNs) as a groundbreaking technology in data processing and communication. It outlines how WSNs, composed of dispersed autonomous sensors, are utilized to monitor physical and environmental factors, transmitting data wirelessly for analysis. The article explores various applications of WSNs in healthcare, national security, emergency response, and infrastructure monitoring, highlighting their roles in enhancing patient care, public health surveillance, border security, disaster management, and military operations. Additionally, it examines the foundational concepts of data privacy in WSNs, focusing on encryption techniques, authentication mechanisms, anonymization techniques, and access control mechanisms. The article also addresses vulnerabilities, threats, and challenges related to data privacy in healthcare and national security contexts, emphasizing regulatory compliance, ethical considerations, and socio-economic factors. Furthermore, it introduces the Diffusion of Innovation Theory as a framework for understanding the adoption of privacy-enhancing technologies in WSNs. Finally, the article reviews empirical studies demonstrating the efficacy of security solutions in preserving data privacy in WSNs, offering insights into advancements in safeguarding sensitive information.

When Your AI Becomes a Target: AI Security Incidents and Best Practices

In contrast to vast academic efforts to study AI security, few real-world reports of AI security incidents exist. Released incidents prevent a thorough investigation of the attackers' motives, as crucial information about the company and AI application is missing. As a consequence, it often remains unknown how to avoid incidents. We tackle this gap and combine previous reports with freshly collected incidents to a small database of 32 AI security incidents. We analyze the attackers' target and goal, influencing factors, causes, and mitigations. Many incidents stem from non-compliance with best practices in security and privacy-enhancing technologies. In the case of direct AI attacks, access control may provide some mitigation, but there is little scientific work on best practices. Our paper is thus a call for action to address these gaps.

Reviewing advancements in privacy-enhancing technologies for big data analytics in an era of increased surveillance

In the contemporary landscape of big data analytics, privacy concerns loom large, exacerbated by escalating surveillance measures. This review delves into the advancements of privacy-enhancing technologies (PETs) amidst this era of heightened scrutiny. The review explores the evolving landscape of PETs, highlighting their pivotal role in safeguarding individual privacy while enabling meaningful data analysis. Firstly, the review elucidates the escalating surveillance environment, characterized by ubiquitous data collection practices and the proliferation of sophisticated monitoring mechanisms. Against this backdrop, the imperative for robust privacy solutions becomes evident. Subsequently, the review navigates through the array of PETs, encompassing differential privacy, homomorphic encryption, secure multi-party computation, and federated learning, among others. Each technology is scrutinized for its efficacy in mitigating privacy risks without compromising analytical utility. Furthermore, the review delineates notable applications of PETs across diverse domains, including healthcare, finance, and social media. Case studies exemplify how PETs facilitate data sharing and collaborative analysis while preserving confidentiality and compliance with regulatory frameworks. Moreover, the review examines the challenges hindering the widespread adoption of PETs, such as computational overhead, interoperability issues, and regulatory ambiguities. Strategies for overcoming these hurdles are elucidated, encompassing advancements in algorithmic efficiency, standardization efforts, and policy advocacy. The review underscores the pivotal role of PETs in reconciling the imperatives of data analytics with the imperatives of privacy protection amidst escalating surveillance. It emphasizes the necessity for interdisciplinary collaboration among researchers, policymakers, and industry stakeholders to foster the development and deployment of effective PET solutions, thereby ensuring a harmonious balance between data utility and individual privacy rights in the digital age.

A comprehensive examination of security and privacy in precision agriculture technologies

Precision agriculture has revolutionized modern farming practices by integrating cutting-edge technologies such as sensors, drones, and data analytics to optimize crop management. While these advancements offer unprecedented benefits in terms of yield optimization and resource efficiency, they also raise significant concerns regarding the security and privacy of sensitive agricultural data. This research paper delves into the intricate landscape of precision agriculture security and privacy, aiming to identify potential threats, vulnerabilities, and the corresponding measures necessary for safeguarding farmers' data and ensuring the sustainability of precision farming practices. The study employs a multidisciplinary approach, combining insights from computer science, agriculture, and privacy studies to provide a comprehensive overview. Through an analysis of existing security frameworks and privacy regulations, this paper proposes recommendations for the development and implementation of robust security protocols and privacy-enhancing technologies tailored to the unique challenges of precision agriculture. The findings of this research contribute to the ongoing dialogue on responsible technology adoption in agriculture, emphasizing the importance of striking a balance between innovation and the protection of farmers' sensitive information in the era of precision farming.

Reviewing the Privacy Implications of Indias Digital Personal Data Protection Act (2023) from Library Contexts

The study reviews the provisions and privacy implications of the India’s Digital Personal Data Protection Act, 2023 (referred to as “DPDP Act” in this study) from the library context. The immense nature of personal data breaches prompted the government to enact the DPDP Act on 11th August 2023. Through the DPDP Act, this study addresses the privacy concerns in the library by articulating the eleven (11) privacy principles, viz., data collection & notice; data retention; data processing; data sharing; users consent; children’s data; user’s rights; users security; reporting; accountability and compensation. These principles can act as privacy guidelines for libraries when negotiating with library vendors; these principles will further guide e-vendors into creating an online environment where user’s privacy rights are protected. Also, this study aims to address the online privacy gaps in libraries by providing additional corrective measures and examples, such as - setting up an itemised privacy policy, a simple policy that is easily accessible, reducing the use of web tracking technologies, encouraging the use of privacy-enhancing technologies, de-identification of patron’s data, and emphasising on user’s right in the web environments. The study may also empower the government and online businesses by preserving the privacy rights of users.

Ethical and legal challenges of AI in marketing: an exploration of solutions

PurposeArtificial intelligence (AI) has sparked interest in various areas, including marketing. However, this exhilaration is being tempered by growing concerns about the moral and legal implications of using AI in marketing. Although previous research has revealed various ethical and legal issues, such as algorithmic discrimination and data privacy, there are no definitive answers. This paper aims to fill this gap by investigating AI’s ethical and legal concerns in marketing and suggesting feasible solutions.Design/methodology/approachThe paper synthesises information from academic articles, industry reports, case studies and legal documents through a thematic literature review. A qualitative analysis approach categorises and interprets ethical and legal challenges and proposes potential solutions.FindingsThe findings of this paper raise concerns about ethical and legal challenges related to AI in the marketing area. Ethical concerns related to discrimination, bias, manipulation, job displacement, absence of social interaction, cybersecurity, unintended consequences, environmental impact, privacy and legal issues such as consumer security, responsibility, liability, brand protection, competition law, agreements, data protection, consumer protection and intellectual property rights are discussed in the paper, and their potential solutions are discussed.Research limitations/implicationsNotwithstanding the interesting insights gathered from this investigation of the ethical and legal consequences of AI in marketing, it is important to recognise the limits of this research. Initially, the focus of this study is confined to a review of the most important ethical and legal issues pertaining to AI in marketing. Additional possible repercussions, such as those associated with intellectual property, contracts and licencing, should be investigated more deeply in future studies. Despite the fact that this study gives various answers and best practices for tackling the stated ethical and legal concerns, the viability and efficacy of these solutions may differ depending on the context and industry. Thus, more research and case studies are required to evaluate the applicability and efficacy of these solutions in other circumstances. This research is mostly based on a literature review and may not represent the experiences or opinions of all stakeholders engaged in AI-powered marketing. Further study might involve interviews or surveys with marketing professionals, customers and other key stakeholders to offer a full knowledge of the practical difficulties and solutions. Because of the rapid speed of technical progress, AI’s ethical and regulatory ramifications in marketing are continually increasing. Consequently, this work should be a springboard for more research and continuing conversations on this subject.Practical implicationsThis study’s findings have several practical implications for marketing professionals. Emphasising openness and explainability: Marketing professionals should prioritise transparency in their use of AI, ensuring that customers are fully informed about data collection and utilisation for targeted advertising. By promoting openness and explainability, marketers can foster customer trust and avoid the negative consequences of a lack of transparency. Establishing ethical guidelines: Marketing professionals need to develop ethical rules for the creation and implementation of AI-powered marketing strategies. Adhering to ethical principles ensures compliance with legal norms and aligns with the organisation’s values and ideals. Investing in bias detection tools and privacy-enhancing technology: To mitigate risks associated with AI in marketing, marketers should allocate resources to develop and implement bias detection tools and privacy-enhancing technology. These tools can identify and address biases in AI algorithms, safeguard consumer privacy and extract valuable insights from consumer data.Social implicationsThis study’s social implications emphasise the need for a comprehensive approach to address the ethical and legal challenges of AI in marketing. This includes adopting a responsible innovation framework, promoting ethical leadership, using ethical decision-making frameworks and conducting multidisciplinary research. By incorporating these approaches, marketers can navigate the complexities of AI in marketing responsibly, foster an ethical organisational culture, make informed ethical decisions and develop effective solutions. Such practices promote public trust, ensure equitable distribution of benefits and risk, and mitigate potential negative social consequences associated with AI in marketing.Originality/valueTo the best of the authors’ knowledge, this paper is among the first to explore potential solutions comprehensively. This paper provides a nuanced understanding of the challenges by using a multidisciplinary framework and synthesising various sources. It contributes valuable insights for academia and industry.

Case study on communicating with research ethics committees about minimizing risk through software: an application for record linkage in secondary data analysis.

In retrospective secondary data analysis studies, researchers often seek waiver of consent from institutional Review Boards (IRB) and minimize risk by utilizing complex software. Yet, little is known about the perspectives of IRB experts on these approaches. To facilitate effective communication about risk mitigation strategies using software, we conducted two studies with IRB experts to co-create appropriate language when describing a software to IRBs. We conducted structured focus groups with IRB experts to solicit ideas on questions regarding benefits, risks, and informational needs. Based on these results, we developed a template IRB application and template responses for a generic study using privacy-enhancing software. We then conducted a three-round Delphi study to refine the template IRB application and the template responses based on expert panel feedback. To facilitate participants' deliberation, we shared the revisions and a summary of participants' feedback during each Delphi round. 11 experts in two focus groups generated 13 ideas on risks, benefits, and informational needs. 17 experts participated in the Delphi study with 13 completing all rounds. Most agreed that privacy-enhancing software will minimize risk, but regardless all secondary data studies have an inherent risk of unexpected disclosures. The majority (84.6%) noted that subjects in retrospective secondary data studies experience no greater risks than the risks experienced in ordinary life in the modern digital society. Hence, all retrospective data-only studies with no contact with subjects would be minimal risk studies. First, we found fundamental disagreements in how some IRB experts view risks in secondary data research. Such disagreements are consequential because they can affect determination outcomes and might suggest IRBs at different institutions might come to different conclusions regarding similar study protocols. Second, the highest ranked risks and benefits of privacy-enhancing software in our study were societal rather than individual. The highest ranked benefits were facilitating more research and promoting responsible data governance practices. The highest ranked risks were risk of invalid results from systematic user error or erroneous algorithms. These societal considerations are typically more characteristic of public health ethics as opposed to the bioethical approach of research ethics, possibly reflecting the difficulty applying a bioethical approach (eg, informed consent) in secondary data studies. Finally, the development of privacy-enhancing technology for secondary data research depends on effective communication and collaboration between the privacy experts and technology developers. Privacy is a complex issue that requires a holistic approach that is best addressed through privacy-by-design principles. Privacy expert participation is important yet often neglected in this design process. This study suggests best practice strategies for engaging the privacy community through co-developing companion documents for software through participatory design to facilitate transparency and communication. In this case study, the final template IRB application and responses we released with the open-source software can be easily adapted by researchers to better communicate with their IRB when using the software. This can help increase responsible data governance practices when many software developers are not research ethics experts.

Protecting Privacy in Digital Records: The Potential of Privacy-Enhancing Technologies

With increased concerns about data protection and privacy over the past several years, and concomitant introduction of regulations restricting access to personal information (PI), archivists in many jurisdictions now must undertake ‘sensitivity reviews’ of archival documents to determine whether they can make those documents accessible to researchers. Such reviews are onerous given increasing volume of records and complex due to how difficult it can be for archivists to identify whether records contain PI under the provisions of various laws. Despite research into the application of tools and techniques to automate sensitivity reviews, effective solutions remain elusive. Not yet explored as a solution to the challenge of enabling access to archival holdings subject to privacy restrictions is the application of privacy-enhancing technologies (PETs) —a class of emerging technologies that rest on the assumption that a body of documents is confidential or private and must remain so. While seemingly being counterintuitive to apply PETs to making archives more accessible, we argue that PETs could provide an opportunity to protect PI in archival holdings whilst still enabling research on those holdings. In this article, to lay a foundation for archival experimentation with use of PETs, we contribute an overview of these technologies based on a scoping review and discuss possible use cases and future research directions.

Toward enhancing web privacy on HTTPS traffic: A novel SuperLearner attack model and an efficient defense approach with adversarial examples

Web privacy-enhancing technologies have been grown to preserve the users' privacy against sophisticated traffic analysis attacks aimed at inferring sensitive information. An especially concerning form of traffic analysis attacks is website fingerprinting attacks, allowing local eavesdroppers to identify a user's visited websites over encrypted connections. Leveraging recent advances in AI methods and aligned with the principle of adversarial thinking (think like an adversary), our objective is to better understand privacy vulnerabilities of HTTPS traffic against ever-evolving traffic analysis attacks. Therefore, as a novel contribution, we propose an HTTPS website fingerprinting attack model called Super-Learner Attack, an ensemble of base learners to exploit the strengths and diminish the weaknesses of the individual base learners, including LogisticRegression, DecisionTreeClassifier, Gaussian Naive Bayes, KNeighborsClassifier, AdaboostClassifier, BaggingClassifier, RandomForestClassifier, and ExtraTreeClassifier. The SuperLearner aims to learn fusion weights in a data-adaptive manner to obtain the optimal combination of the base learners. The proposed attack attains a high accuracy of over 97% on HTTPS traffic, outperforming existing attack models. Furthermore, responding to the challenge of website fingerprinting attacks and in direct response to the SuperLearner Attacker, we propose HTTPS Obfuscation Defender. This is a novel and highly effective defense strategy rooted in deception. This strategy disrupts classification by skillfully introducing fake packets into real flows, obfuscating patterns, and disrupting classification. Unlike previous methods, our approach leverages adversarial example algorithms originally designed for image analysis, to generate maximal obfuscation in encrypted HTTPS traffic. It generates perturbations for encrypted HTTPS traffic through the utilization of three well-established adversarial example algorithms. Our experimental results demonstrate that HTTPS Obfuscation Defender significantly reduces the accuracy of website fingerprinting from 97.2% to 2.89%, even when an attacker attempts to adapt to the defense and retrain a classifier using defended traffic. Moreover, our proposed defense ensures a minimal impact on time and bandwidth, making it a highly practical and resource-efficient defense approach.

Privacy-First Paradigm for Dynamic Consent Management Systems: Empowering Data Subjects through Decentralized Data Controllers and Privacy-Preserving Techniques

This paper explicitly focuses on utilizing blockchain technology in dynamic consent management systems with privacy considerations. While blockchain offers improved security, the potential impact on entities’ privacy must be considered. Through a critical investigation of available contributions to the present state of the art of blockchain-based dynamic consent management systems, we highlight the limitations of plaintext storage and the processing of subject data/consent on the blockchain, which can compromise privacy. We stress the significance of keeping encrypted subject data/consent on the blockchain and sharing it in encrypted form with data controllers and requesters to guarantee privacy and security. Our proposed model demonstrates the usefulness of privacy-preserving techniques, underscoring the decentralization of the abstract entity data controller to enhance subject data/consent privacy. Additionally, we suggest the integration of privacy-enhancing technologies such as secure multi-party computation, homomorphic encryption, and differential privacy with blockchain to accomplish both security and privacy, aligning with the data sharing practices outlined in the General Data Protection Regulation (GDPR) in Europe.

Изучение технологий, повышающих конфиденциальность

Confidential information is a valuable asset for organizations, and its protection is critical to ensure business security and maintain the trust of customers and partners. The issue of data privacy is becoming increasingly relevant in light of the growing number of data leaks both around the world and in Russia. This paper discusses various privacy-enhancing technologies. In this regard, new technologies are emerging that allow you to protect data from unauthorized access and maintain their confidentiality. This paper discusses various technologies that increase data privacy, such as data anonymization, pseudonymization, multiple computing methods, blockchain, differential confidentiality, homomorphic encryption, their advantages and disadvantages. The classification of technologies that increase privacy is given.

Preserving data privacy in machine learning systems

The wide adoption of Machine Learning to solve a large set of real-life problems came with the need to collect and process large volumes of data, some of which are considered personal and sensitive, raising serious concerns about data protection. Privacy-enhancing technologies (PETs) are often indicated as a solution to protect personal data and to achieve a general trustworthiness as required by current EU regulations on data protection and AI. However, an off-the-shelf application of PETs is insufficient to ensure a high-quality of data protection, which one needs to understand. This work systematically discusses the risks against data protection in modern Machine Learning systems taking the original perspective of the data owners, who are those who hold the various data sets, data models, or both, throughout the machine learning life cycle and considering the different Machine Learning architectures. It argues that the origin of the threats, the risks against the data, and the level of protection offered by PETs depend on the data processing phase, the role of the parties involved, and the architecture where the machine learning systems are deployed. By offering a framework in which to discuss privacy and confidentiality risks for data owners and by identifying and assessing privacy-preserving countermeasures for machine learning, this work could facilitate the discussion about compliance with EU regulations and directives.We discuss current challenges and research questions that are still unsolved in the field. In this respect, this paper provides researchers and developers working on machine learning with a comprehensive body of knowledge to let them advance in the science of data protection in machine learning field as well as in closely related fields such as Artificial Intelligence.

Using the blockchain to enable transparent and auditable processing of personal data in cloud- based services: Lessons from the Privacy-Aware Cloud Ecosystems (PACE) project

The architecture of cloud-based services is typically opaque and intricate. As a result, data subjects cannot exercise adequate control over their personal data, and overwhelmed data protection authorities must spend their limited resources in costly forensic efforts to ascertain instances of non-compliance. To address these data protection challenges, a group of computer scientists and socio-legal scholars joined forces in the Privacy-Aware Cloud Ecosystems (PACE) project to design a blockchain-based privacy-enhancing technology (PET). This article presents the fruits of this collaboration, highlighting the capabilities and limits of our PET, as well as the challenges we encountered during our interdisciplinary endeavour. In particular, we explore the barriers to interdisciplinary collaboration between law and computer science that we faced, and how these two fields’ different expectations as to what technology can do for data protection law compliance had an impact on the project's development and outcome. We also explore the overstated promises of techno-regulation, and the practical and legal challenges that militate against the implementation of our PET: most industry players have no incentive to deploy it, the transaction costs of running it make it prohibitively expensive, and there are significant clashes between the blockchain's decentralised architecture and GDPR's requirements that hinder its deployability. We share the insights and lessons we learned from our efforts to overcome these challenges, hoping to inform other interdisciplinary projects that are increasingly important to shape a data ecosystem that promotes the protection of our personal data.