Privacy Model Research Articles

Exploring Use of Explanative Illustrations to Communicate Differential Privacy Models

Proper communication is key to the adoption and implementation of differential privacy (DP). In this work, we designed explanative illustrations of three DP models (Central DP, Local DP, Shuffler DP) to help laypeople conceptualize how random noise is added to protect individuals’ privacy and preserve group utility. Following a pilot survey and an interview, we conducted an online experiment ( N = 300) exploring participants’ comprehension, privacy and utility perception, and data-sharing decisions across the three DP models. We obtained empirical evidence showing participants’ acceptance of the Shuffler DP model for data privacy protection. We discuss the implications of our findings.

Differential privacy model for blockchain based smart home architecture

Secure and private communications using the Internet of Things (IoT) pose several challenges for smart home systems. In particular, data collected from IoT devices comprise sensitive personal information such as biomedical data, financial data, and location and activity data. Recent research looks into the use of blockchain in smart home systems, protecting the privacy of the data in use. Such solutions need to address the issue of privacy using a formal and mathematical model for data privacy due to the vulnerability associated with privacy-preserving blockchain networks. In the present paper, our approach aims to provide a privacy-preserving data aggregation mechanism in the context of Smart Homes that agree to contribute their data to a cloud server using machine learning to improve services for home users. We propose the use of differential privacy, a powerful concept in privacy preserving schemes to provide formal assurances about how much information is leaked using a privacy budget. The main purpose of using such a privacy-preserving scheme is to limit what can be inferred about individual training data from the model. Our techniques use a R′enyi differential privacy (RDP) machine learning scheme and are based on a variant of the stochastic gradient descent function. The performance of our proposed framework is evaluated using three public datasets: UNSW-NB15, NSL-KDD, and ToN-IoT datasets. Our findings show that differential private models can provide privacy protection against attackers by sacrificing a substantial amount of model utility. Therefore, we propose an empirical value of ϵ, that can optimally balances utility and privacy for the current smart home scenario datasets.

Attribute disclosure risk for k-anonymity: the case of numerical data

k-Anonymity is one of the most well-known privacy models. Internal and external attacks were discussed for this privacy model, both focusing on categorical data. These attacks can be seen as attribute disclosure for a particular attribute. Then, p-sensitivity and p-diversity were proposed as solutions for these privacy models. That is, as a way to avoid attribute disclosure for this very attribute. In this paper we discuss the case of numerical data, and we show that attribute disclosure can also take place. For this, we use well-known rules to detect sensitive cells in tabular data protection. Our experiments show that k-anonymity is not immune to attribute disclosure in this sense. We have analyzed the results of two different algorithms for achieving k-anonymity. First, MDAV as a way to provide microaggregation and k-anonymity. Second, Mondrian. In fact, to our surprise, the number of cells detected as sensitive is quite significant, and there are no fundamental differences between Mondrian and MDAV. We describe the experiments considered, and the results obtained. We define dominance rule compliant and p%-rule compliant k-anonymity for k-anonymity taking into account attribute disclosure. We conclude with an analysis and directions for future research.

Towards a Universal Privacy Model for Electronic Health Record Systems: An Ontology and Machine Learning Approach

This paper proposed a novel privacy model for Electronic Health Records (EHR) systems utilizing a conceptual privacy ontology and Machine Learning (ML) methodologies. It underscores the challenges currently faced by EHR systems such as balancing privacy and accessibility, user-friendliness, and legal compliance. To address these challenges, the study developed a universal privacy model designed to efficiently manage and share patients’ personal and sensitive data across different platforms, such as MHR and NHS systems. The research employed various BERT techniques to differentiate between legitimate and illegitimate privacy policies. Among them, Distil BERT emerged as the most accurate, demonstrating the potential of our ML-based approach to effectively identify inadequate privacy policies. This paper outlines future research directions, emphasizing the need for comprehensive evaluations, testing in real-world case studies, the investigation of adaptive frameworks, ethical implications, and fostering stakeholder collaboration. This research offers a pioneering approach towards enhancing healthcare information privacy, providing an innovative foundation for future work in this field.

Federated Calibration and Evaluation of Binary Classifiers

We address two major obstacles to practical deployment of AI-based models on distributed private data. Whether a model was trained by a federation of cooperating clients or trained centrally, (1) the output scores must be calibrated, and (2) performance metrics must be evaluated --- all without assembling labels in one place. In particular, we show how to perform calibration and compute the standard metrics of precision, recall, accuracy and ROC-AUC in the federated setting under three privacy models ( i ) secure aggregation, ( ii ) distributed differential privacy, ( iii ) local differential privacy. Our theorems and experiments clarify tradeoffs between privacy, accuracy, and data efficiency. They also help decide if a given application has sufficient data to support federated calibration and evaluation.

AGAPECert: An Auditable, Generalized, Automated, Privacy-Enabling Certification Framework With Oblivious Smart Contracts

This paper introduces AGAPECert, an Auditable, Generalized, Automated, Privacy-Enabling, Certification framework capable of performing auditable computation on private data and reporting real-time aggregate certification status without disclosing underlying private data. AGAPECert utilizes a novel mix of trusted execution environments, blockchain technologies, and a real-time graph-based API standard to provide automated, oblivious, and auditable certification. Our technique allows a privacy-conscious data owner to run pre-approved Oblivious Smart Contract code in their own environment on their own private data to produce Private Automated Certifications. These certifications are verifiable, purely functional transformations of the available data, enabling a third party to trust that the private data must have the necessary properties to produce the resulting certification. Recently, a multitude of solutions for certification and traceability in supply chains have been proposed. These often suffer from significant privacy issues because they tend to take a" shared, replicated database" approach: every node in the network has access to a copy of all relevant data and contract code to guarantee the integrity and reach consensus, even in the presence of malicious nodes. In these contexts of certifications that require global coordination, AGAPECert can include a blockchain to guarantee ordering of events, while keeping a core privacy model where private data is not shared outside of the data owner's own platform. AGAPECert contributes an open-source certification framework that can be adopted in any regulated environment to keep sensitive data private while enabling a trusted automated workflow.

Bitcoin network-based anonymity and privacy model for metaverse implementation in Industry 5.0 using linear Diophantine fuzzy sets

AbstractMetaverse is a new technology expected to generate economic growth in Industry 5.0. Numerous studies have shown that current bitcoin networks offer remarkable prospects for future developments involving metaverse with anonymity and privacy. Hence, modelling effective Industry 5.0 platforms for the bitcoin network is crucial for the future metaverse environment. This modelling process can be classified as multiple-attribute decision-making given three issues: the existence of multiple anonymity and privacy attributes, the uncertainty related to the relative importance of these attributes and the variability of data. The present study endeavours to combine the fuzzy weighted with zero inconsistency method and Diophantine linear fuzzy sets with multiobjective optimisation based on ratio analysis plus the multiplicative form (MULTIMOORA) to determine the ideal approach for metaverse implementation in Industry 5.0. The decision matrix for the study is built by intersecting 22 bitcoin networks to support Industry 5.0's metaverse environment with 24 anonymity and privacy evaluation attributes. The proposed method is further developed to ascertain the importance level of the anonymity and privacy evaluation attributes. These data are used in MULTIMOORA. A sensitivity analysis, correlation coefficient test and comparative analysis are performed to assess the robustness of the proposed method.

Data Privacy Best Practices of A Local Higher Educational Institution: A Model for Governance

The passage of the Data Privacy Act of 2012, establishes a new data privacy framework for all industries and sectors, whether in public or private. Different agencies’ policies have aimed to foster innovation and the development of commercial and public sector technical skills. The hit COVID-19 pandemic pushed academic institutions for a paradigm shift in the delivery of instructional and administrative services in online platforms. This study assessed Laguna State Polytechnic University’s (LSPU) data privacy policies and governance transformation to determine its best practices and establish an informational privacy framework. LSPU commenced its full compliance in 2017, hence the researcher chose the respondents whose employment at the university is between 2017 and 2021 to ensure they have sufficient experience and skill in the implementation of the Data Privacy Act of 2012 and its rules and regulation in the university. Questionnaires were utilized to collect information, and interviews with unit heads and university staff validated the results. Based on the study’s hypothesis, it is determined that the governance of existing data privacy policies at the institution is completely established. Since the results of the analysis revealed a significant correlation, the respondents strongly support a very high degree of data privacy implementation oversight. Further, using the path-analysis technique, it was found that governance has a positive effect on the current practices of data privacy in the university. Finally, the study revealed that when there is a certain policy change responsive to the data privacy law that is to be implemented in the university, it is expected that practices in the implementation of data privacy law will show significant change. Keywords: Data Governance, Data Practices, Informational Governance, Data Privacy Model

A Multi-Input Fusion Model for Privacy and Semantic Preservation in Facial Image Datasets

The widespread application of multimedia technologies such as video surveillance, online meetings, and drones facilitates the acquisition of a large amount of data that may contain facial features, posing significant concerns with regard to privacy. Protecting privacy while preserving the semantic contents of facial images is a challenging but crucial problem. Contemporary techniques for protecting the privacy of images lack the incorporation of the semantic attributes of faces and disregard the protection of dataset privacy. In this paper, we propose the Facial Privacy and Semantic Preservation (FPSP) model that utilizes similar facial feature replacement to achieve identity concealment, while adding semantic evaluation to the loss function to preserve semantic features. The proposed model is versatile and efficient in different task scenarios, preserving image utility while concealing privacy. Our experiments on the CelebA dataset demonstrate that the model achieves a semantic preservation rate of 77% while concealing the identities in facial images in the dataset.

Privacy-Preserved Framework for Short-Term Probabilistic Net Energy Forecasting

This paper develops a differential privacy (DP) model for short-term probabilistic energy forecasting at the low-aggregate level. The method first takes probabilistic forecasting elements from a predictor and injects noise into the mean of the forecast based on the Laplace mechanism to guarantee privacy on the mean. The standard deviation is then carefully perturbed (enlarged) to ensure the forecast to be released contains a desirable confidence interval (CI), here 95%. By doing so, customers' privacy is protected, while the user, such as grid operators or retail providers, receives the forecast containing 95% CI of the original forecast. The predictor used to capture the model and data uncertainties is based on a Bayesian neural network (BNN), which is also benchmarked against a Gaussian Process (GP). The simulations are carried out for different levels of privacy, <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><inline-formula><tex-math notation="LaTeX">$\varepsilon$</tex-math></inline-formula></b> , and the obtained trend provides decision-makers with a clear idea of determining the appropriate <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><inline-formula><tex-math notation="LaTeX">$\varepsilon$</tex-math></inline-formula></b> . The study found that the forecasting error is smoothed out on the used dataset for <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><inline-formula><tex-math notation="LaTeX">$\varepsilon \geq 0.6$</tex-math></inline-formula></b> . The proposed model is an output perturbation approach. Accordingly, the obtained results are compared with an input perturbation approach, showing that the proposed DP model gives the best accuracy/privacy trade-off for users. Furthermore, this study includes rooftop PV generation behind the meter, which is one of the recent transitioning challenges in energy forecasting. Detailed analysis reveals that forecasting is more challenging for periods before sunset when PV generation drop coincides with the common changing point of households' activities.

Fighting against COVID‐19: Innovations and applications

Fighting against <scp>COVID</scp>‐19: Innovations and applications

IoT based smart home automation using blockchain and deep learning models.

For the past few years, the concept of the smart house has gained popularity. The major challenges concerning a smart home include data security, privacy issues, authentication, secure identification, and automated decision-making of Internet of Things (IoT) devices. Currently, existing home automation systems address either of these challenges, however, home automation that also involves automated decision-making systems and systematic features apart from being reliable and safe is an absolute necessity. The current study proposes a deep learning-driven smart home system that integrates a Convolutional neural network (CNN) for automated decision-making such as classifying the device as "ON" and "OFF" based on its utilization at home. Additionally, to provide a decentralized, secure, and reliable mechanism to assure the authentication and identification of the IoT devices we integrated the emerging blockchain technology into this study. The proposed system is fundamentally comprised of a variety of sensors, a 5 V relay circuit, and Raspberry Pi which operates as a server and maintains the database of each device being used. Moreover, an android application is developed which communicates with the Raspberry Pi interface using the Apache server and HTTP web interface. The practicality of the proposed system for home automation is tested and evaluated in the lab and in real-time to ensure its efficacy. The current study also assures that the technology and hardware utilized in the proposed smart house system are inexpensive, widely available, and scalable. Furthermore, the need for a more comprehensive security and privacy model to be incorporated into the design phase of smart homes is highlighted by a discussion of the risks analysis' implications including cyber threats, hardware security, and cyber attacks. The experimental results emphasize the significance of the proposed system and validate its usability in the real world.

A Confusion Method for the Protection of User Topic Privacy in Chinese Keyword-based Book Retrieval

In this article, aiming at a Chinese keyword-based book search service, from a technological perspective, we propose to modify a user query sequence carefully to confuse the user query topics and thus protect the user topic privacy on the untrusted server, without compromising the accuracy of each book search service. First, we propose a client-based framework for the privacy protection of book search, and then a privacy model to formulate the constraints in terms of accuracy, efficiency, and security, which the cover queries generated based on a user query sequence should meet. Second, we present a modification algorithm for a user query sequence, based on some heuristic strategies, which can quickly generate a cover query sequence meeting the privacy model by replacing, deleting, and adding keywords for each user query. Finally, both theoretical analysis and experimental evaluation demonstrate the effectiveness of the proposed approach, i.e., which can improve the security of users’ topic privacy on the untrusted server without compromising the efficiency, accuracy, and usability of an existing Chinese keyword book search service, so it has a positive impact for the construction of a privacy-preserving text retrieval platform under an untrusted network environment.

A trust, privacy and security model for e-commerce in Nigeria

Today, digital technologies has permeated every sector of human life from communication to medicine to education to transportation and most recently shopping.. The mode of buying and selling by Nigerians have moved from traditional to digital, whereby consumers buy and sell goods online. The online presence of commerce is known as Electronic commerce or E-commerce and it is an initiative amongst other emerging digital sectors in Nigeria that has witnessed substantive growth recently. However, the issues of security, privacy of data and the trust endeared by users on the initiative remains a challenging one. Presently in Nigeria, the solutions to combat security concerns of E-commerce are technically inclined and therefore do not sufficiently capture the solution paradigm. Due to a rise in these E-commerce‘s security-related concerns in Nigeria as discovered by taking random samples of the population used for the survey, this research proposes a Security, Trust and Privacy model for the survival of E-commerce in Nigeria.

An empirical investigation of the social web gendered privacy model

ABSTRACT According to Thelwall's ([2011]. “Privacy and Gender in the Social Web.” In Privacy Online. Perspectives on Privacy and Self-Disclosure in the Social Web, edited by S. Trepte and L. Reinecke, 251–266. Springer) social web gendered privacy model, gender differences in offline privacy risks (i.e. experiences of privacy threat, such as aggressive behaviour or betrayal of a secret) and communication qualities transfer to online contexts, and shape gender differences in online privacy perceptions and behaviours. Using representative data (n = 1,043) from four times of measurement over the course of three years, a structural equation model was set up. I found that people with negative offline privacy experiences at T1 express higher online privacy concerns a year later (T2), and take more actions to protect their online privacy at T3. When adequate privacy protection is established, people disclose more personal information privately (e.g. messenger), but not in public (e.g. status updates) at T4. Females reported more negative offline privacy experiences, offline social support, and offline information disclosure. In contrast to the model’s claims, in an online context, men disclose more personal information both privately and publicly. The results provide evidence for the proposed relations of Thelwall's (2011) model: Offline conditions transfer to online contexts and shape social media users’ privacy perception. However, the findings do not support the idea that women are an especially vulnerable group in online settings.

Dynamic Privacy Choices

I study a dynamic model of consumer privacy and platform data collection. In each period consumers choose their level of platform activity. Greater activity generates more information about the consumer, thereby increasing platform profits. When the platform can commit to the future privacy policy, it collects information by committing to gradually decreasing the level of privacy protection. In the long run consumers lose privacy and receive low payoffs but choose high activity levels. In contrast, the platform with weaker commitment power may attain the commitment outcome or fail to collect any data depending on consumer expectations regarding future privacy protection. (JEL C93, D11, D82, D83, D84, L86)

Shuffle Differential Private Data Aggregation for Random Population

Bridging the advantages of differential privacy in both centralized model (i.e., high accuracy) and local model (i.e., minimum trust), the shuffle privacy model has potential applications in many privacy-sensitive scenarios, such as mobile user data aggregation and federated learning. Since messages from users are anonymized by semi-trusted shufflers (e.g., anonymous channels, edge servers), every user could hide message among other users’ messages and inject only part of noises (a.k.a. privacy amplification). However, existing works assume that the participating user population is known in advance, which is unrealistic for dynamic environments (e.g., mobile computing, vehicular networks). In this work, we study the shuffle privacy model with a random participating population, and give privacy amplification bounds for population size with commonly encountered binomial, Poisson, sub-Gaussian distribution and etc. For further improving accuracy, we formulate and derive optimal dummy sizes for both non-adaptive and adaptive dummies. Finally, to break the error barrier due to the constraint of sending one single message per user, we design a multi-message shuffle private protocol supporting random population. Experiment results show that our approaches reduce more than <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$60\%$</tex-math></inline-formula> error when compared to the local model and naive approaches. We hope this work provides tailored solutions of shuffle privacy for dynamic mobile/distributed computing.

A distributed computing model for big data anonymization in the networks.

Recently big data and its applications had sharp growth in various fields such as IoT, bioinformatics, eCommerce, and social media. The huge volume of data incurred enormous challenges to the architecture, infrastructure, and computing capacity of IT systems. Therefore, the compelling need of the scientific and industrial community is large-scale and robust computing systems. Since one of the characteristics of big data is value, data should be published for analysts to extract useful patterns from them. However, data publishing may lead to the disclosure of individuals' private information. Among the modern parallel computing platforms, Apache Spark is a fast and in-memory computing framework for large-scale data processing that provides high scalability by introducing the resilient distributed dataset (RDDs). In terms of performance, Due to in-memory computations, it is 100 times faster than Hadoop. Therefore, Apache Spark is one of the essential frameworks to implement distributed methods for privacy-preserving in big data publishing (PPBDP). This paper uses the RDD programming of Apache Spark to propose an efficient parallel implementation of a new computing model for big data anonymization. This computing model has three-phase of in-memory computations to address the runtime, scalability, and performance of large-scale data anonymization. The model supports partition-based data clustering algorithms to preserve the λ-diversity privacy model by using transformation and actions on RDDs. Therefore, the authors have investigated Spark-based implementation for preserving the λ-diversity privacy model by two designed City block and Pearson distance functions. The results of the paper provide a comprehensive guideline allowing the researchers to apply Apache Spark in their own researches.

Enhancing Utility in Anonymized Data against the Adversary’s Background Knowledge

Recent studies have shown that data are some of the most valuable resources for making government policies and business decisions in different organizations. In privacy preserving, the challenging task is to keep an individual’s data protected and private, and at the same time the modified data must have sufficient accuracy for answering data mining queries. However, it is difficult to implement sufficient privacy where re-identification of a record is claimed to be impossible because the adversary has background knowledge from different sources. The k-anonymity model is prone to attribute disclosure, while the t-closeness model does not prevent identity disclosure. Moreover, both models do not consider background knowledge attacks. This paper proposes an anonymization algorithm called the utility-based hierarchical algorithm (UHRA) for producing k-anonymous t-closed data that can prevent background knowledge attacks. The proposed framework satisfies the privacy requirements using a hierarchical approach. Finally, to enhance utility of the anonymized data, records are moved between different anonymized groups, while the requirements of the privacy model are not violated. Our experiments indicate that our proposed algorithm outperforms its counterparts in terms of data utility and privacy.

An artificial intelligence lightweight blockchain security model for security and privacy in IIoT systems

The Industrial Internet of Things (IIoT) promises to deliver innovative business models across multiple domains by providing ubiquitous connectivity, intelligent data, predictive analytics, and decision-making systems for improved market performance. However, traditional IIoT architectures are highly susceptible to many security vulnerabilities and network intrusions, which bring challenges such as lack of privacy, integrity, trust, and centralization. This research aims to implement an Artificial Intelligence-based Lightweight Blockchain Security Model (AILBSM) to ensure privacy and security of IIoT systems. This novel model is meant to address issues that can occur with security and privacy when dealing with Cloud-based IIoT systems that handle data in the Cloud or on the Edge of Networks (on-device). The novel contribution of this paper is that it combines the advantages of both lightweight blockchain and Convivial Optimized Sprinter Neural Network (COSNN) based AI mechanisms with simplified and improved security operations. Here, the significant impact of attacks is reduced by transforming features into encoded data using an Authentic Intrinsic Analysis (AIA) model. Extensive experiments are conducted to validate this system using various attack datasets. In addition, the results of privacy protection and AI mechanisms are evaluated separately and compared using various indicators. By using the proposed AILBSM framework, the execution time is minimized to 0.6 seconds, the overall classification accuracy is improved to 99.8%, and detection performance is increased to 99.7%. Due to the inclusion of auto-encoder based transformation and blockchain authentication, the anomaly detection performance of the proposed model is highly improved, when compared to other techniques.