Privacy Framework Research Articles

Academic Literature on Compliance Programs, ESG, Corporate Governance, Fraud Prevention, Human Rights, Corruption, Data Protection, and SDGs

This academic summary provides an overview of key themes in recent open-access articles pertaining to compliance programs, environmental, social, and governance (ESG) practices, corporate governance, fraud prevention, human rights, corruption, data protection, and Sustainable Development Goals (SDGs) as set forth by the United Nations. Scholars have extensively explored the significance of compliance programs in organizations, evaluating their role in ensuring ethical practices, legal adherence, and risk mitigation. Additionally, research has focused on the adoption and impact of ESG strategies, addressing their influence on corporate decision-making, sustainability, and stakeholder engagement. Corporate governance remains a vital area of investigation, investigating board structures, executive compensation, and the effectiveness of governance mechanisms in enhancing firm performance and accountability. Simultaneously, efforts to combat fraud and corruption have led to analyses of fraud detection techniques, whistleblowing systems, and anti-corruption measures. The literature also highlights the evolving landscape of data protection, emphasizing regulatory developments, privacy frameworks, and organizational responsibilities in safeguarding data. Furthermore, there is a growing emphasis on the integration of human rights principles into business practices, exploring the implications of human rights violations and strategies for promoting responsible corporate behavior. Lastly, articles have explored the alignment of corporate practices with the SDGs, examining businesses' contributions to global sustainability goals.

Submodular maximization over data streams with differential privacy noise

In the big data era, data often comes in the form of streams and fast data stream analysis has recently attracted intensive research interest. Submodular optimization naturally appears in many streaming data applications such as social network influence maximization with the property of diminishing returns. However, in a practical setting, streaming data frequently comes with noises that are small but significant enough to impact the optimality of submodular optimization solutions. Following the framework of differential privacy (DP), this paper considers a streaming model with DP noise that is small by construction. Within this noisy streaming model, the paper strives to address the general problem of submodular maximization with a cardinality constraint. The main theoretical result we obtained is a streaming algorithm that is one-pass and has an approximation guarantee of 1(2+(1+1k)2)(1+1k)−δ for any δ>0. Finally, we implement the algorithm and evaluate it against several baseline methods. Numerical results support the practical performance of our algorithm across several real datasets.

Differentially private transferrable deep learning with membership-mappings

Despite a recent surge of research interest in privacy and transferrable deep learning, optimizing the tradeoff between privacy requirements and performance of machine learning models remains a challenge. This motivates the development of an approach that optimizes both privacy-preservation mechanism and learning of the deep models for achieving a robust performance. This paper considers the problem of semi-supervised transfer and multi-task learning under differential privacy framework. An alternative conception of deep autoencoder, referred to as Conditionally Deep Membership-Mapping Autoencoder (CDMMA), is considered for transferrable deep learning. Under practice-oriented settings, an analytical solution for the learning of CDMMA can be derived by means of variational optimization. The paper proposes a transfer and multi-task learning approach that combines CDMMA with a tailored noise adding mechanism to transfer knowledge from source to target domain in a privacy-preserving manner.

Confidentiality Protection in the 2020 US Census of Population and Housing

In an era where external data and computational capabilities far exceed statistical agencies’ own resources and capabilities, they face the renewed challenge of protecting the confidentiality of underlying microdata when publishing statistics in very granular form and ensuring that these granular data are used for statistical purposes only. Conventional statistical disclosure limitation methods are too fragile to address this new challenge. This article discusses the deployment of a differential privacy framework for the 2020 US Census that was customized to protect confidentiality, particularly the most detailed geographic and demographic categories, and deliver controlled accuracy across the full geographic hierarchy.

The Fundraiser's Transfer of Personal Data from the European Union to the United States in Context of Crowdfunding Activities

European start-up companies must overcome more ‘transfer hurdles’ when personal data is transferred from the European Union to the US (United States of America) as part of crowdfunding campaign activities. Transfer of personal data is commonly not associated with (small scale) crowdfunding activities. However, the strict rules of the EU GDPR (European General Data Protection Regulation) on safeguarding personal data apply to all companies when data is transferred from the EU to the US - regardless the size of the business.
 This article identifies exchange of personal data that takes place between primarily fundraiser and crowdfunding service provider in different steps of fundraising campaigns. The framework for rewardbased crowdfunding for goods production that is provided by the US based Indiegogo platform is used as example and context. The article highlights by way of example the obligations that must be met by European fundraisers as "data controllers" when personal data is transferred to Indiegogo. No easy solutions are provided by either European Union or national data protection authorities on how to establish an adequate level of personal data protection. Paradigms on how to secure transfer of personal data to third countries are available in form of so-called standard contractual clauses, but still conditions for transfer of personal data from Europe to the US are hard to comply with. Apart from entering into an inter partes agreement on use of standard contractual clauses with the crowdfunding platform provider, a European fundraiser must furthermore make a so-called "transfer impact assessment" to ensure that third party access to personal data is avoided. In the case of transfer of personal data from the EU to the US the fundraiser must consider using encryption of data as a "supplementary measure" to block third party access. Encryption of data is however not suitable for exchange of data in a dynamic crowdfunding campaign so other means for protection of data must be found and applied.
 The reason and explanation for making data transfers from the EU to the US that hard for e.g., fundraisers are thus to be found at interstate level in the relation between the EU and the US. According to EU law, more specifically the GDPR and several of the provision of the Charter of Fundamental Rights of the European Union, US security legislation authorises a disproportionate access for US intelligence services to citizens' personal data. A solution on manageable transfer of personal data from the EU to the US may be found before the end of 2022, since a new TADP (Trans-Atlantic Data Privacy Framework) is currently being negotiated between EU and US at top politician level. However, the implementation of the TADP may take som time since the EU legislative framework needs adjustments to make the new transfer possibilities operational.

SaferHome: Interactive Physical and Digital Smart Home Dashboards for Communicating Privacy Assessments to Owners and Bystanders

Private homes are increasingly becoming smart spaces. While smart homes promise comfort, they expose most intimate spaces to security and privacy risks. Unfortunately, most users today are not equipped with the right tools to assess the vulnerabilities or privacy practices of smart devices. Further, users might lose track of the devices installed in their homes or are unaware of devices placed by a partner or host. We developed SaferHome, an interactive digital-physical privacy framework, to provide smart home users with security and privacy assessments and a sense of device location. SaferHome includes a digital list view and physical and digital dashboards that map real floor plans. We evaluated SaferHome with eight households in the wild. We find that users adopted various strategies to integrate the dashboards into their understanding and interpretation of smart home privacy. We present implications for the design of future smart home privacy frameworks that are impacted by technical affinity, device types, device ownership, and tangibility of assessments.

A scalable, secure, and semantically interoperable client for cloud-enabled Demand Response

Demand Response (DR) is becoming a cornerstone element in the current energy sector, particularly for the EU energy markets. For this reason, considerable effort has been spent on standardising demand response data models. As a result, there is an ever-growing number of demand response proposals based on these standards. However, these proposals are usually centralised, and those that rely on cloud solutions use the cloud as a centralised data store assuming that the data is already homogenised when stored, i.e. all the data has the same format and model. Nevertheless, in practise, DR proposals rely on several components that provide data in heterogeneous formats and models. Furthermore, the different DR standards define models for different data formats that hinder data exchange between different DR systems. In this article, a generic tool called CIM is presented, which allows existing DR systems to distribute their components in the cloud, providing a solid security and privacy framework for data exchange. In addition, the CIM implements a semantic interoperability layer that is capable of translating data into a normalised form when exchanged so that it can be transparently consumed by DR components. Experiments advocate the CIM as a solution for DR systems to decentralise their architectures and exchange heterogeneous data even with other DR systems that follow different DR standards.

Role of Privacy Protection in Business Models for Sustainability

Purpose: The principal purpose of this article is to address a critical issue emerging in the realm of interorganizational dependencies heavily impacted by digitalization, namely developing business models that would protect privacy in a sustainable way. On the one hand, companies have been jointly proposing, creating, delivering, and capturing value through an excessive, unethical exploitation of personal data and information. On the other, restricting and controlling flows of data and information hampers the processes that lead to social well-being. This article reflects on this paradox by building on the theories of business models for sustainability and contextual integrity, while offering a holistic conceptual narrative guiding the sustainable transition towards digital equity and inclusivity. Design/methodology/approach: This conceptual article can be classified as a theory synthesis paper with the ambition to achieve an outcome that enhances knowledge on concepts and a phenomenon by a conceptual integration across two different, previously unconnected literature streams and theories. Findings: This article suggests that businesses which play any role in transmission of data and information cannot be sustainable without protecting privacy as a social value. Furthermore, it argues that privacy cannot be protected without addressing the appropriateness of both flow and use of data and information with respect to all involved stakeholders. Ultimately, via linking two distinct yet interrelated and rigorously developed research streams, a heuristic framework for privacy and sustainability in business models is proposed as a system of key considerations for managers to apply in assessing and planning a business practice, so it protects privacy in a sustainable way. Originality/value: The key theoretical contribution of this article can be considered twofold. Firstly, it unfolds the relevance of privacy protection for the stream of business model research directed toward sustainable development in a way that is theoretically rigorous, complementary with the stakeholder theory, and reflecting the changing interorganizational dependencies affected by digitalization. Secondly, it contributes to the contemporary debate on privacy as a social value through identifying theoretically thorough avenue for adapting the theory of contextual integrity to a social domain where value proposition, creation, delivery, and capture with and for stakeholders involves transmission of data and information.

Differentially Private K-Means Clustering Applied to Meter Data Analysis and Synthesis

The proliferation of smart meters has resulted in a large amount of data being generated. It is increasingly apparent that methods are required for allowing a variety of stakeholders to leverage the data in a manner that preserves the privacy of the consumers. The sector is scrambling to define policies, such as the so called ‘15/15 rule’, to respond to the need. However, the current policies fail to adequately guarantee privacy. In this paper, we address the problem of allowing third parties to apply <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> -means clustering, obtaining customer labels and centroids for a set of load time series by applying the framework of differential privacy. We leverage the method to design an algorithm that generates differentially private synthetic load data consistent with the labeled data. We test our algorithm’s utility by answering summary statistics such as average daily load profiles for a 2-dimensional synthetic dataset and a real-world power load dataset.

How American Businesses Could Approach Trade with Companies in South America that Are Located in Adequate and Non-Adequate Countries

This essay discusses the legal data privacy issues faced when doing business with a European Union (EU) member or a GDPR-compliant country that is not a member of the EU. The EU data transfer requirements are briefly explained, followed by a description of the South American nations that are General Data Protection Regulation (GDPR)-complaint or near GDPR- compliant, including Argentina, Brazil, Chile, and Uruguay. The paper talks about whether the United States or any of the states in the Union can be considered by the European Commission (EC) to be an adequate country and the impacts of the United States not being an adequate country. The former United States Privacy Shield (Shield) and its predecessor, the International Safe Harbor Privacy Principles (ISHPP), both of which were invalidated by the EC. Although the United States and the EU recently announced the Trans-Atlantic Data Privacy Framework (TADPF), the EC is anticipated to invalidate this framework. It is recommended that companies employ the pre-approved standard contractual clauses (SCCs) as the least risky endeavor to assure personal data privacy. The paper then turns to the issues involved in leveraging existing privacy policies. In this regard, the United States’ sectoral approach to privacy is examined. The leverage issues that exist when interacting with GDPR-complaint countries are considered. Two lists of recommendations are presented, the first list being more general-purposes, while the second list is specific. The paper concludes by observing that a firm should analyze the privacy laws under which it is covered, select the most inclusive policies and procedures so that the company is compliant with the GDPR and state and federal sectoral laws, and implement the resulting conservative privacy framework.

BDTwin: An Integrated Framework for Enhancing Security and Privacy in Cybertwin-Driven Automotive Industrial Internet of Things

The rapid development of the automotive Industrial Internet of Things requires secure networking infrastructure toward digitalization. Cybertwin (CT) is a next-generation networking architecture that serves as a communication, and digital asset owner, and can make the Vehicle-to-Everything (V2X) network flexible and secure. However, CT itself can publish end users’ digital assets to other entities as a service, making data security and privacy major obstacles in the realization of V2X applications. Motivated from the aforementioned discussion, this article presents BDTwin, a blockchain and deep-learning-based integrated framework to enhance security and privacy in CT-driven V2X applications. Specifically, a blockchain scheme is designed to ensure secure communication among vehicles, roadside units, CT-edge server, and cloud server using a smart contract-based enhance-Proof-of-Work (ePoW) and Zero Knowledge Proof (ZKP)-based verification process. Smart contracts are used to enforce rules and regulations that govern the behavior of V2X entities in a nondeniable and automated manner. In a deep-learning scheme, an autoregressive-deep variational autoencoder model is combined with attention-based bidirectional long short-term memory (A-BLSTM) for automatic feature extraction and attack detection by analyzing CT-edge servers data in a V2X environment. Security analysis and experimental results using two different sources, ToN-IoT and CICIDS-2017 show the superiority of the proposed BDTwin framework over some baseline and recent state-of-the-art techniques.

Griffin: Real-Time Network Intrusion Detection System via Ensemble of Autoencoder in SDN

Many efforts have been devoted to the development of efficient Network Intrusion Detection System (NIDS) using machine learning approaches in Software-defined Network (SDN). Unfortunately, existing solutions failed to detect real-time and zero-day attacks due to their limited throughput and prior knowledge-based detection. To this end, we propose Griffin, a NIDS that uses unsupervised machine learning expertise to detect both known and zero-day intrusion attacks in real-time with high accuracy. Specifically, Griffin uses an efficient feature extraction framework to capture the sequential features of the traffic packets. Then, it utilizes cluster analysis to reduce the feature scale to achieve low throughput. Moreover, an ensemble autoencoder is built automatically to further extract features with low complexity and high precision to train the model. We evaluate the accuracy, robustness, and complexity of the system using open datasets. The result shows that Griffin’s complexity is about 40% lower, and its accuracy is at most 19% higher than existing NIDS.Additionally, even in the situation with evasion, the Griffin has at most 9% decrease of AUC, which is a good performance compared with other solutions. Furthermore, this paper also utilizes the differential privacy framework during training autoencoders to protect datasets’ privacy which is inherent in machine learning approaches.

Reform in Australia: A Focus on Informed Consent

This article analyses the Australian privacy framework in the context of both the Australian Competition and Consumer Commission’s Digital Platforms Inquiry (DPI) and the Consumer Data Right (CDR). This analysis extends to informed consent and attitudes to unfairness and unconscionability. The article offers potential solutions to the current patchwork approach which go further than the Government response to the DPI. It argues that the Australian Government’s response is not an adequate response nor a set of suitable solutions to the problem. The article proposes a two-pronged approach that recognizes the urgency of the issue through the suggestion of a series of ‘quick policy wins’ that will result in more meaningful and effective protection for consumers and further systemic, long-term recommendations for change that can be achieved through policy development, further consultation and integration with other existing legislation. The quick policy wins centre on three specific changes, including definitional updates, content and structure of online standard form agreements and enforcement, penalties and sanctions, and long-term solutions. The long-term solutions are proposed to include regulation of website design, better integration of the laws, regulators and enforcement bodies, a faster, more consistent pace of policy review and recognition of the societal and human benefit of informed consent to online standard form agreements. Australia, Digital Platforms, Consumer Data Right, Informed Consent, Reform

A multimodal differential privacy framework based on fusion representation learning

Differential privacy mechanisms vary in modalities, and there have been many methods implementing differential privacy on unimodal data. Few studies focus on unifying them to protect multimodal data, though privacy protection of multimodal data is of great significance. In our work, we propose a multimodal differential privacy protection framework. Firstly, we use multimodal representation learning to fuse different modalities and map them to the same subspace. Then based on this representation, we use the Local Differential Privacy (LDP) mechanism to protect data. We propose two protection methods for low-dimensional and high-dimensional fusion tensors respectively. The former is based on Binary Encoding, and the latter is based on multi-dimensional Fourier Transform. To the best of our knowledge, we are the first to propose LDP-based methods for the representation learning of multimodal fusion. Experimental results demonstrate the flexibility of our framework where both approaches show efficient performance as well as high data utility.

Context Optimized and Spatial Aware Dummy Locations Generation Framework for Location Privacy

Context Optimized and Spatial Aware Dummy Locations Generation Framework for Location Privacy

An Enhanced Dynamic Nonlinear Polynomial Integrity-Based QHCP-ABE Framework for Big Data Privacy and Security

Topics such as computational sources and cloud-based transmission and security of big data have turned out to be a major new domain of exploration due to the exponential evolution of cloud-based data and grid facilities. Various categories of cloud services have been utilized more and more widely across a variety of fields like military, army systems, medical databases, and more, in order to manage data storage and resource calculations. Attribute-based encipherment (ABE) is one of the more efficient algorithms that leads to better consignment and safety of information located within such cloud-based storage amenities. Many outmoded ABE practices are useful for smaller datasets to produce fixed-size cryptograms with restricted computational properties, in which their characteristics are measured as evidence and stagnant standards used to generate the key, encipherment, and decipherment means alike. To surmount the existing problems with such limited methods, in this work, a dynamic nonlinear poly randomized quantum hash system is applied to enhance the safety of cloud-based information. In the proposed work, users’ attributes are guaranteed with the help of a dynamic nonlinear poly randomized equation to initialize the chaotic key, encipherment, and decipherment. In this standard, structured and unstructured big data from clinical datasets are utilized as inputs. Real-time simulated outcomes demonstrate that the stated standard has superior exactness, achieving over 90% accuracy with respect to bit change and over 95% accuracy with respect to dynamic key generation, encipherment time, and decipherment time compared to existing models from the field and literature. Experimental results are demonstrated that the proposed cloud security standard has a good efficiency in terms of key generation, encoding, and decoding process than the conventional methods in a cloud computing environment.

Federated one-class collaborative filtering via privacy-aware non-sampling matrix factorization

In this paper, we study an emerging and important recommendation problem called federated one-class collaborative filtering (FOCCF). Specifically, we aim to build a recommendation model by exploiting each user’s one-class or implicit feedback in a distributed and privacy-aware manner rather than collecting and learning from data in a central server. For the studied problem, there are three important issues, i.e., recommendation accuracy, privacy and efficiency. As a response, we start from the state-of-the-art one-class collaborative filtering (OCCF) method, i.e., non-sampling matrix factorization (NSMF), and propose a novel federated recommendation framework called privacy-aware NSMF (P-NSMF). Our P-NSMF protects user privacy well without sacrificing recommendation accuracy and contains two variants, i.e., P-NSMF(ALS) and P-NSMF(BGD), which are based on alternating least squares (ALS) and batch gradient descent (BGD), respectively. Moreover, we design an improved strategy called group-wise concealing and adopt a secure aggregation technique in our framework for privacy and efficiency. We then analyze the security and complexity of our P-NSMF, and conduct extensive experiments on four public datasets. In particular, compared with the existing methods, our P-NSMF outperforms them in terms of recommendation accuracy, privacy and efficiency, which are important merits for real deployment.

Politics by Automatic Means? A Critique of Artificial Intelligence Ethics at Work.

Calls for “ethical Artificial Intelligence” are legion, with a recent proliferation of government and industry guidelines attempting to establish ethical rules and boundaries for this new technology. With few exceptions, they interpret Artificial Intelligence (AI) ethics narrowly in a liberal political framework of privacy concerns, transparency, governance and non-discrimination. One of the main hurdles to establishing “ethical AI” remains how to operationalize high-level principles such that they translate to technology design, development and use in the labor process. This is because organizations can end up interpreting ethics in an ad-hoc way with no oversight, treating ethics as simply another technological problem with technological solutions, and regulations have been largely detached from the issues AI presents for workers. There is a distinct lack of supra-national standards for fair, decent, or just AI in contexts where people depend on and work in tandem with it. Topics such as discrimination and bias in job allocation, surveillance and control in the labor process, and quantification of work have received significant attention, yet questions around AI and job quality and working conditions have not. This has left workers exposed to potential risks and harms of AI. In this paper, we provide a critique of relevant academic literature and policies related to AI ethics. We then identify a set of principles that could facilitate fairer working conditions with AI. As part of a broader research initiative with the Global Partnership on Artificial Intelligence, we propose a set of accountability mechanisms to ensure AI systems foster fairer working conditions. Such processes are aimed at reshaping the social impact of technology from the point of inception to set a research agenda for the future. As such, the key contribution of the paper is how to bridge from abstract ethical principles to operationalizable processes in the vast field of AI and new technology at work.

Deletion inference, reconstruction, and compliance in machine (un)learning

Privacy attacks on machine learning models aim to identify the data that is used to train such models. Such attacks, traditionally, are studied on static models that are trained once and are accessible by the adversary. Motivated to meet new legal requirements, many machine learning methods are recently extended to support machine unlearning, i.e., updating models as if certain examples are removed from their training sets, and meet new legal requirements. However, privacy attacks could potentially become more devastating in this new setting, since an attacker could now access both the original model before deletion and the new model after the deletion. In fact, the very act of deletion might make the deleted record more vulnerable to privacy attacks. Inspired by cryptographic definitions and the differential privacy framework, we formally study privacy implications of machine unlearning. We formalize (various forms of) deletion inference and deletion reconstruction attacks, in which the adversary aims to either identify which record is deleted or to reconstruct (perhaps part of) the deleted records. We then present successful deletion inference and reconstruction attacks for a variety of machine learning models and tasks such as classification, regression, and language models. Finally, we show that our attacks would provably be precluded if the schemes satisfy (variants of) deletion compliance (Garg, Goldwasser, and Vasudevan, Eurocrypt’20).

Strengthening the Legal Framework for Personal Data Protection in Nigeria

Advancement in Information Communication Technology (ICT) has brought to the fore the need for the protection of individuals’ personal data. In today’s digital age, the personal data of individuals are routinely collected and stored in databases of both private and public establishments. Such personally identifiable information can easily be analyzed with fascinating accuracy, rapidly transmitted, and put to unimaginable uses. This situation has placed the regulation of personal data collection and uses on the front burner in many nations. The weak or total absence of regulation of personal data poses serious challenges to the security of lives and property of individuals and can constitute a serious disincentive for the adoption of beneficial technology. Employing the doctrinal methodology, this article examines the legal framework for personal data protection in Nigeria with the aim of assessing the adequacy or otherwise of relevant extant regulations in protecting the personal data of Nigerians and other people doing business in Nigeria. Looking at the state of the law in some developed and developing countries, the paper notes that the current state of regulation in Nigeria is still a far cry from what obtains in most countries of the global North and some sister African countries. It, therefore, concludes that there is a compelling need for a stronger regulatory framework for data privacy in Nigeria.