Personally Identifiable Information Research Articles

Methodological developments in administrative data linkage for cross cutting policy relevant research: Working towards a sustainable data pipeline

ObjectivesDevelop administrative linkages within a national Trusted Research Environment (TRE) that hosts Longitudinal Population Study (LPS) data for over 20 LPS. We will describe the methodological development carried out to enable linkage to administrative datasets. These linked administrative data will support research for public good, informing policy and practice.
 MethodsThe first sets of administrative data under consideration in this Feasibility Study are from the Department of Work and Pensions (DWP), the Department for Education (DfE) and HM Revenue and Customs (HMRC). Working with UK Government departments through a Task & Finish group we have gathered input from DWP, HMRC and DfE and Office for National Statistics (ONS) data sharing experts. The Task & Finish group identified three pragmatic data linkage and data sharing models, that would enable data to be linked via a newly designed secure data pipeline in a legal, secure, and trustworthy manner for all stakeholders.
 ResultsTo encourage sustainability and acceptability, a model designed to be maintained over a long period is based on the re-use of Departmental Personal Identifiable Information (PII) – i.e., name, date of birth, gender, National Insurance number - and attribute data already deposited by the Departments into ONS. ONS will develop for the linkage and extraction of ONS Data into the TRE a system which conducts, and quality assesses the linkage; minimises the Departmental data to participants within the TRE only and the variables specified in the agreements; and, de-identifies the data to their DEA processing standards. The minimised and functionally anonymous data extract will be securely transferred for ingest and integration into the TRE enabling researchers to address a wider range of questions for public benefit.
 ConclusionThis is a model for efficient and low-burden linkages to inform cross cutting research. It will form part of a responsive UK data science capability which can inform government research needs and be used to meet future crisis e.g. new pandemics, the impacts of climate change or economic shocks.

Web-Based Application Based on Human-in-the-Loop Deep Learning for Deidentifying Free-Text Data in Electronic Medical Records: Development and Usability Study.

The narrative free-text data in electronic medical records (EMRs) contain valuable clinical information for analysis and research to inform better patient care. However, the release of free text for secondary use is hindered by concerns surrounding personally identifiable information (PII), as protecting individuals' privacy is paramount. Therefore, it is necessary to deidentify free text to remove PII. Manual deidentification is a time-consuming and labor-intensive process. Numerous automated deidentification approaches and systems have been attempted to overcome this challenge over the past decade. We sought to develop an accurate, web-based system deidentifying free text (DEFT), which can be readily and easily adopted in real-world settings for deidentification of free text in EMRs. The system has several key features including a simple and task-focused web user interface, customized PII types, use of a state-of-the-art deep learning model for tagging PII from free text, preannotation by an interactive learning loop, rapid manual annotation with autosave, support for project management and team collaboration, user access control, and central data storage. DEFT comprises frontend and backend modules and communicates with central data storage through a filesystem path access. The frontend web user interface provides end users with a user-friendly workspace for managing and annotating free text. The backend module processes the requests from the frontend and performs relevant persistence operations. DEFT manages the deidentification workflow as a project, which can contain one or more data sets. Customized PII types and user access control can also be configured. The deep learning model is based on a Bidirectional Long Short-Term Memory-Conditional Random Field (BiLSTM-CRF) with RoBERTa as the word embedding layer. The interactive learning loop is further integrated into DEFT to speed up the deidentification process and increase its performance over time. DEFT has many advantages over existing deidentification systems in terms of its support for project management, user access control, data management, and an interactive learning process. Experimental results from DEFT on the 2014 i2b2 data set obtained the highest performance compared to 5 benchmark models in terms of microaverage strict entity-level recall and F1-scores of 0.9563 and 0.9627, respectively. In a real-world use case of deidentifying clinical notes, extracted from 1 referral hospital in Sydney, New South Wales, Australia, DEFT achieved a high microaverage strict entity-level F1-score of 0.9507 on a corpus of 600 annotated clinical notes. Moreover, the manual annotation process with preannotation demonstrated a 43% increase in work efficiency compared to the process without preannotation. DEFT is designed for health domain researchers and data custodians to easily deidentify free text in EMRs. DEFT supports an interactive learning loop and end users with minimal technical knowledge can perform the deidentification work with only a shallow learning curve.

Privacy Preservation Using Machine Learning in the Internet of Things

The internet of things (IoT) has prepared the way for a highly linked world, in which everything is interconnected, and information exchange has become more easily accessible via the internet, making it feasible for various applications that enrich the quality of human life. Despite such a potential vision, users’ privacy on these IoT devices is a significant concern. IoT devices are subject to threats from hackers and malware due to the explosive expansion of IoT and its use in commerce and critical infrastructures. Malware poses a severe danger to the availability and reliability of IoT devices. If left uncontrolled, it can have profound implications, as IoT devices and smart services can collect personally identifiable information (PII) without the user’s knowledge or consent. These devices often transfer their data into the cloud, where they are stored and processed to provide the end users with specific services. However, many IoT devices do not meet the same security criteria as non-IoT devices; most used schemes do not provide privacy and anonymity to legitimate users. Because there are so many IoT devices, so much malware is produced every day, and IoT nodes have so little CPU power, so antivirus cannot shield these networks from infection. Because of this, establishing a secure and private environment can greatly benefit from having a system for detecting malware in IoT devices. In this paper, we will analyze studies that have used ML as an approach to solve IoT privacy challenges, and also investigate the advantages and drawbacks of leveraging data in ML-based IoT privacy approaches. Our focus is on using ML models for detecting malware in IoT devices, specifically spyware, ransomware, and Trojan horse malware. We propose using ML techniques as a solution for privacy attack detection and test pattern generation in the IoT. The ML model can be trained to predict behavioral architecture. We discuss our experiments and evaluation using the “MalMemAnalysis” datasets, which focus on simulating real-world privacy-related obfuscated malware. We simulate several ML algorithms to prove their capabilities in detecting malicious attacks against privacy. The experimental analysis showcases the high accuracy and effectiveness of the proposed approach in detecting obfuscated and concealed malware, outperforming state-of-the-art methods by 99.50%, and would be helpful in safeguarding an IoT network from malware. Experimental analysis and results are provided in detail.

Microsoft Purview: A System for Central Governance of Data

Modern data estates are spread across data located on premises, on the edge and in one or more public clouds, spread across various sources like multiple relational databases, file and storage systems, and no-SQL systems, both operational and analytic; this phenomenon is referred to as data sprawl. Data administrators who wish to enforce compliance across the entire organization have to inventory their data, identify what parts of it are sensitive, and govern the sensitive data appropriately --- across the entirety of their sprawling data estate. Today, governance of data is completely siloed; each of the data subsystems has its own (and varied) governance features. Policies applied to sensitive data are applied piece-meal by iterating over all the data sources in a custom language specific to each source. This makes data governance cumbersome, error-prone (because a given policy must be manually enforced across different subsystems, inconsistencies can easily arise), and expensive. This paper presents Microsoft Purview , a service for unified governance of the entire data estate of an organization from a single central pane of glass. The Purview service consists of three parts: (1) a Data Map or metadata catalog that is populated by automated scanning of data sources in the organization, (2) a system to store and manage sensitivity classification of data, and (3) a policy system that enables data security officers to author and implement policies that span the entire organization, e.g., a policy that says, "Non-full-time employees should be denied access to data classified as PII (Personally Identifiable Information.") Purview transforms data governance across a complex data estate by offering the ability to govern centrally and automating data discovery, classification and policy enforcement. While other commercial catalog systems also build a global catalog, Purview is unique in its support for policies. It is also distinguished by covering both structured and unstructured data, thanks to its deep integration with Office 365 and its governance framework; indeed, "Microsoft Purview" represents a new unified offering that combines Office 365 governance and what was formerly a service for governing structured data called "Azure Purview". By integrating with Office 365's Rights Management Service, Purview offers central governance over structured data stored in databases and stores, reports in systems such as Power BI, as well as document data stored in Office 365. The Purview vision is to make the metadata in the Data Map increasingly richer through further automation and curation support and to use this 360 degree view of the data estate to support a wide range of governance policies, ranging from access control to lifecycle management (e.g., retention, deletion, restricting data movement). This paper covers the design and implementation challenges in building the Purview service for Attribute-Based Access Control (ABAC) policies, focusing specifically on a detailed description of its integration with Azure SQL Database. We illustrate the power of unifying Office 365 governance with structured data governance through Purview policies that enforce consistent access control even as data flows between Office 365 and structured data engines like Azure SQL Database. We also describe the results of our empirical evaluation of the performance overheads imposed by Purview.

Defending Your Mobile Fortress: An In-Depth Look at on-Device Trojan Detection in Machine Learning: Systematic Literature Review

Mobile app trojans are becoming an increasingly serious threat to personal information security. They can cause severe damage by exposing sensitive and personally-identifying information to malicious actors. This paper’s contribution is a comprehensive review of the attack vectors for trojan attacks, and ways to eliminate the risks posed by attack vectors and generate settlement automatically. As such, such attacks must be prevented. In this study, we explore to find how to detect the trojan attack in detail, and the way that we know in machine learning. A review is conducted on the state-of-the-art methods using the preferred reporting items for reviews and meta-analyses (PRISMA) guidelines. We review literature from several publications and analyze the use of machine learning for on-device trojan detection. This review provides evidence for the effectiveness of machine learning in detecting such threats. The current trend shows that signature-based analysis using various metadata, such as permission, intent, API and system calls, and network analysis, are capable of detecting trojan attacks before and after the initial infection

Enabling Efficient Personally Identifiable Information Detection with Automatic Consent Discovery

Personal data leakage prevention has now become a critical issue for implementing data management and sharing in many industries. Several data privacy regulations such as General Data Protection Regulation (GDPR), Health Insurance Portability and Accountability Act (HIPPA), California Consumer Privacy Act (CCPA), and Thailand's Personal Data Protection Act (PDPA) have been issued to enforce organizations to collect, process, and transfer personally identifiable information (PII) securely. In this paper, we propose a design and development of PII RapidDiscover, an efficient Thai and English PII discovery system featured with automatic consent discovery. At the core of our proposed system, we introduce the PII scanning algorithm based on the Presidio library and a natural language processing (NLP) technique to improve the scan result of PII written in Thai and English. Finally, we conducted the experiments to demonstrate the efficiency of our proposed system.

Rainbow: reliable personally identifiable information retrieval across multi-cloud

Personally identifiable information (PII) refers to any information that links to an individual. Sharing PII is extremely useful in public affairs yet hard to implement due to the worries about privacy violations. Building a PII retrieval service over multi-cloud, which is a modern strategy to make services stable where multiple servers are deployed, seems to be a promising solution. However, three major technical challenges remain to be solved. The first is the privacy and access control of PII. In fact, each entry in PII can be shared to different users with different access rights. Hence, flexible and fine-grained access control is needed. Second, a reliable user revocation mechanism is required to ensure that users can be revoked efficiently, even if few cloud servers are compromised or collapse, to avoid data leakage. Third, verifying the correctness of received PII and locating a misbehaved server when wrong data are returned is crucial to guarantee user’s privacy, but challenging to realize. In this paper, we propose Rainbow, a secure and practical PII retrieval scheme to solve the above issues. In particular, we design an important cryptographic tool, called Reliable Outsourced Attribute Based Encryption (ROABE) which provides data privacy, flexible and fine-grained access control, reliable immediate user revocation and verification for multiple servers simultaneously, to support Rainbow. Moreover, we present how to build Rainbow with ROABE and several necessary cloud techniques in real world. To evaluate the performance, we deploy Rainbow on multiple mainstream clouds, namely, AWS, GCP and Microsoft Azure, and experiment in browsers on mobile phones and computers. Both theoretical analysis and experimental results indicate that Rainbow is secure and practical.

Predicting Phishing Victimization: Comparing Prior Victimization, Cognitive, and Emotional Styles, and Vulnerable or Protective E-mail Strategies

ABSTRACT Phishing victimization is prevalent and results in theft of personal identifiable information (PII) or installing malware to steal PII. Drawing upon social psychological and criminological theories, we conducted a prospective study to assess three groups of predictors to being phished or not: a) prior victimization; b) protective or vulnerable habitual strategies, and c) emotional and cognitive decision-making styles. Students (N = 236) completed a survey assessing these predictors and then about 4 weeks later received a phishing e-mail using the university’s phishing testing system. The e-mail requested that they click on a link and enter their student ID to avoid having their account blocked. About half (50.8%) clicked on the link, and 81.6% of those phished entered their PII. Individuals who had low avoidant style and high generalized anxiety were four times more likely to be phished, after controlling for the significant effects of vulnerable habitual strategies and using dating apps. Machine learning analyses also found cognitive styles and generalized anxiety are the better predictors of getting phished compared to vulnerable and protective strategies and prior victimization. These findings suggest that cybersecurity training needs to be expanded to address the emotional and cognitive processing of deceptive appeals in e-mails.

Understanding Policy and Technical Aspects of AI-enabled Smart Video Surveillance to Address Public Safety

Recent advancements in artificial intelligence (AI) have seen the emergence of smart video surveillance (SVS) in many practical applications, particularly for building safer and more secure communities in our urban environments. Cognitive tasks, such as identifying objects, recognizing actions, and detecting anomalous behaviors, can produce data capable of providing valuable insights to the community through statistical and analytical tools. However, artificially intelligent surveillance systems design requires special considerations for ethical challenges and concerns. The use and storage of personally identifiable information (PII) commonly pose an increased risk to personal privacy. To address these issues, this paper identifies the privacy concerns and requirements needed to address when designing AI-enabled smart video surveillance. Further, we propose the first end-to-end AI-enabled privacy-preserving smart video surveillance system that holistically combines computer vision analytics, statistical data analytics, cloud-native services, and end-user applications. Finally, we propose quantitative and qualitative metrics to evaluate intelligent video surveillance systems. The system shows the 17.8 frame-per-second (FPS) processing in extreme video scenes. However, considering privacy in designing such a system results in preferring the pose-based algorithm to the pixel-based one. This choice resulted in dropping accuracy in both action and anomaly detection tasks. The results drop from 97.48% to 73.72% in anomaly detection and 96% to 83.07% in the action detection task. On average, the latency of the end-to-end system is 36.1 seconds.

Is personally identifiable information really more valuable? Evidence from consumers' willingness-to-accept valuation of their privacy information

Information privacy is not yet a mature “product.” The amount of monetary reward that firms should pay consumers to obtain their data is unknown. This paper measures consumers' valuation of two types of personal information—personally identifiable information (PII) and non-PII, with the willing-to-accept (WTA) payoffs using a multi-study mixed-methods approach. We conducted two realistic auction experiments with 150 and 415 participants, respectively, as well as one interview study with 31 participants and a meta-analysis study to explain international differences in WTA prices between China and other countries. We found that while consumers claimed that PII was more valuable than non-PII in the interview study, they did not demand a higher WTA price when monetizing PII in the two experimental studies. However, consumers became more cautious and provided fewer data items when dealing with PII, compared to non-PII. Consumers may ask for a higher price for non-PII than PII when they need to disclose more information. They also strategically select information being disclosed, if possible, to reduce privacy risk. Context only matters in WTA price for certain types of data, which is medical PII data in our case. Specifically, the WTA price for PII is higher in the medical information context than in the shopping information context. The meta-analysis shows that individualism and uncertainty avoidance are two key cultural dimensions explaining international differences in WTA prices.

E-resource librarians perceptions on library patron privacy

Libraries are tasked with upholding a code of ethics for patron privacy according to the Library Bill of Rights and the American Library Association's (ALA) Code of Ethics. However, patrons' personal identifiable information (PII) is being shared through various channels. As libraries move to more technical and digital based collections this creates more challenges to safeguarding patrons' privacy. This technical environment is where information now serves as a platform for library assessment analysis, access for patrons, and demonstration of impact.This study examines the values of e-resource librarians about privacy, the environmental factors influencing decisions, and if the e-resource librarian has a role in their organization's privacy standards. Data was collected through surveys, and the study found that there is a direct correlation between an organization having a policy on privacy and the e-resource librarians' values being represented in their role. There are also indications that the e-resource librarian believes their employer shares their values on privacy. Lastly, the study revealed two major external driving forces on privacy policy are vendors and US privacy policy.

A combined Blockchain and zero-knowledge model for healthcare B2B and B2C data sharing

The two main forms of healthcare data exchange among entities are business-to-business (B2B) and business-to-customer (B2C). The former uses the electronic data interchange (EDI) technology between healthcare institutions, while the latter is usually conducted by providing web-based interfaces for patients. This research argues that both forms have inherent security and privacy weaknesses. Furthermore, patients lack appropriate transparency and control over their own Personally Identifiable Information (PII). We explore the issues of medical record exchange, analyze them and suggest appropriate solutions in the form of a new model to mitigate them. The vulnerabilities, ranging from critical to minor, include the possibility of Man-in-The-Middle (MiTM) and supply chain attacks, weak cryptography, repudiable transactions, single points of failure (SPOF), and poor access controls. A novel model will be presented in this research for healthcare data sharing which applies the best security practices. The proposed unified model will counter the listed vulnerabilities. It automates the healthcare processes in decentralized architecture by utilizing the smart contracts for B2C transactions such as medicine purchase. The model is based on the Blockchain and zero-knowledge proofs. It is made with novel controls which represent the latest advancements in cybersecurity. It has the potential of setting a new cornerstone.

PriMed: Private federated training and encrypted inference on medical images in healthcare

AbstractIn healthcare, patient information is a sparse critical asset considered as private data and is often protected by law. It is also the domain which is least explored in the field of Machine Learning. The main reason for this is to build efficient artificial intelligence (AI) based models for preliminary diagnosis of various diseases, it would require a large corpus of data which can be obtained by pooling in patient information from multiple sources. However, for these sources to agree to sharing their data across distributed systems for training algorithms and models, there has to be an assurance that there will be no disclosure of the personally identifiable information (PII) of the respective Data Owners. This paper proposes PriMed, an approach to build robust privacy preserving additions to convolutional neural networks (CNN) for training and performing inference on medical images without compromising privacy. Since privacy of the data is preserved, large amounts of data can be effectively accumulated to increase the accuracy and efficiency of AI models in the field of healthcare. This involves implementing a hybrid of privacy‐enhancing techniques like Federated Learning, Differential Privacy, and Homomorphic Encryption to provide a private and secure environment for learning through data.

Social engineering and the disclosure of personal identifiable information: Examining the relationship and moderating factors using a population-based survey experiment

People tend to disclose personal identifiable information (PII) that could be used by cybercriminals against them. Often, persuasion techniques are used by cybercriminals to trick people to disclose PII. This research investigates whether people can be made less susceptible to persuasion by reciprocation (i.e., making people feel obligated to return a favour) and authority, particularly in regard to whether information security knowledge and positive affect moderate the relation between susceptibility to persuasion and disclosing PII. Data are used from a population-based survey experiment that measured the actual disclosure of PII in an experimental setting ( N = 2426). The results demonstrate a persuasion–disclosure link, indicating that people disclose more PII when persuaded by reciprocation, but not by authority. Knowledge of information security was also found to relate to disclosure. People disclosed less PII when they possessed more knowledge of information security. Positive affect was not related to the disclosure of PII. And contrary to expectations, no moderating effects were found of information security knowledge nor positive affect on the persuasion–disclosure link. Possible explanations are discussed, as well as limitations and future research directions.

Don’t Be a Victim During a Pandemic! Analysing Security and Privacy Threats in Twitter During COVID-19

There has been a huge spike in the usage of social media platforms during the COVID-19 lockdowns. These lockdown periods have resulted in a set of new cybercrimes, thereby allowing attackers to victimise social media users with a range of threats. This paper performs a large-scale study to investigate the impact of a pandemic and the lockdown periods on the security and privacy of social media users. We analyse 10.6 Million COVID-related tweets from 533 days of data crawling and investigate users’ security and privacy behaviour in three different periods (i.e., before, during, and after the lockdown). Our study shows that users unintentionally share more personal identifiable information when writing about the pandemic situation (e.g., sharing nearby coronavirus testing locations) in their tweets. The privacy risk reaches 100% if a user posts three or more sensitive tweets about the pandemic. We investigate the number of suspicious domains shared on social media during different phases of the pandemic. Our analysis reveals an increase in the number of suspicious domains during the lockdown compared to other lockdown phases. We observe that IT, Search Engines, and Businesses are the top three categories that contain suspicious domains. Our analysis reveals that adversaries’ strategies to instigate malicious activities change with the country’s pandemic situation.

Enhancing cyber security in health care industry by using ISO 27001 accreditation

Healthcare Industry plays a pivotal role in every one's life and with rapid advancements in cyber-attack vectors, threat actors and their strategies it has in-need created a necessity and a challenge to the numerous organizations and to the Governments as well, to stand guard and secure the institutes and the data stored with them. To design a secure healthcare system involves several considerations to protect sensitive patient data and ensure the confidentiality, integrity, and availability of information. Lot of work has been published on cyber security along with importance of protecting the Personally Identifiable Information (PII) and patient health records stored in hospitals, and also comparisons were made between paid or licensed tools and open source; however, implementation of the tools in real time was not in place where the financial limitations are a real concern and security is a need. This paper has given insight into important parameters such as risk assessment and security policies etc.

Improving Cohort-Hospital Matching Accuracy through Standardization and Validation of Participant Identifiable Information.

Linking very large, consented birth cohorts to birthing hospitals clinical data could elucidate the lifecourse outcomes of health care and exposures during the pregnancy, birth and newborn periods. Unfortunately, cohort personally identifiable information (PII) often does not include unique identifier numbers, presenting matching challenges. To develop optimized cohort matching to birthing hospital clinical records, this pilot drew on a one-year (December 2020-December 2021) cohort for a single Australian birthing hospital participating in the whole-of-state Generation Victoria (GenV) study. For 1819 consented mother-baby pairs and 58 additional babies (whose mothers were not themselves participating), we tested the accuracy and effort of various approaches to matching. We selected demographic variables drawn from names, DOB, sex, telephone, address (and birth order for multiple births). After variable standardization and validation, accuracy rose from 10% to 99% using a deterministic-rule-based approach in 10 steps. Using cohort-specific modifications of the Australian Statistical Linkage Key (SLK-581), it took only 3 steps to reach 97% (SLK-5881) and 98% (SLK-5881.1) accuracy. We conclude that our SLK-5881 process could safely and efficiently achieve high accuracy at the population level for future birth cohort-birth hospital matching in the absence of unique identifier numbers.

Examining the barriers, facilitators and attitudes towards COVID-19 vaccine and public health measures for black communities in Canada: a qualitative study protocol

IntroductionBlack communities claim the highest number of cases and deaths due to COVID-19 in Canada. Generating culturally/contextually appropriate public health measures and strategies for vaccine uptake in black communities within...

The development of a mobile app-focused deduplication strategy for the Apple Heart Study that informs recommendations for future digital trials.

An app-based clinical trial enrolment process can contribute to duplicated records, carrying data management implications. Our objective was to identify duplicated records in real time in the Apple Heart Study (AHS). We leveraged personal identifiable information (PII) to develop a dissimilarity score (DS) using the Damerau-Levenshtein distance. For computational efficiency, we focused on four types of records at the highest risk of duplication. We used the receiver operating curve (ROC) and resampling methods to derive and validate a decision rule to classify duplicated records. We identified 16,398 (4%) duplicated participants, resulting in 419,297 unique participants out of a total of 438,435 possible. Our decision rule yielded a high positive predictive value (96%) with negligible impact on the trial's original findings. Our findings provide principled solutions for future digital trials. When establishing deduplication procedures for digital trials, we recommend collecting device identifiers in addition to participant identifiers; collecting and ensuring secure access to PII; conducting a pilot study to identify reasons for duplicated records; establishing an initial deduplication algorithm that can be refined; creating a data quality plan that informs refinement; and embedding the initial deduplication algorithm in the enrolment platform to ensure unique enrolment and linkage to previous records.

Cyber Security for Internet of Things

Abstract: The Internet of things has gained intense popularity in the past few years. It has become one of the most important technologies. However along with gaining popularity, threats related to cyber security have increased. As the name suggests, Internet of Things, it is completely based on the internet which means a hacker can find a weaklink in the network and exfiltrate data to the cloud and threaten to keep, delete or make the data public. Thisincludes sensitive data, personally identifiable information (PII), protected health information (PHI), personal information, intellectual property, data, and governmentaland industry information systems. Cybersecurity is a set ofprocesses, tools and frameworks to protect networks, devices, programs and data from cyber attacks.This paper highlights the different types of Cyber attacks possible on IoT and suggests few mitigations towards it