Flimma: a federated and privacy-aware tool for differential gene expression analysis

Aggregating transcriptomics data across hospitals can increase sensitivity and robustness of differential expression analyses, yielding deeper clinical insights. As data exchange is often restricted by privacy legislation, meta-analyses are frequently employed to pool local results. However, the accuracy might drop if class labels are inhomogeneously distributed among cohorts. Flimma (https://exbio.wzw.tum.de/flimma/) addresses this issue by implementing the state-of-the-art workflow limma voom in a federated manner, i.e., patient data never leaves its source site. Flimma results are identical to those generated by limma voom on aggregated datasets even in imbalanced scenarios where meta-analysis approaches fail.

Collection of Data on Persons Living With Dementia Who Go Missing: First Responder Perspectives

While it is commonly cited that 60% of persons living with dementia (PLWD) wander, it is unclear whether this number reflects global contexts. Population aging has created a pressing need for the development of programs to mitigate the risks of PLWD from getting lost and going missing. Such programs would require a national strategy for the collection and integration of data on missing incidents involving this population. This study is a first step to inform such a strategy. The purposes were to: 1) identify approaches to data collection on missing persons incidents involving PLWD among Canadian police and search and rescue (SAR) organizations; 2) describe the foreseeable challenges associated with developing a national data collection strategy. We used generic qualitative description to generate data with fifteen key informants. Virtual semi-structured interviews were completed and transcribed verbatim. Content analysis and trustworthiness strategies guided analysis and rigor. Our findings indicate that police and SAR organizations collect a multitude of data pertaining to missing incidents involving PLWD. However, there is a lack of standardization in data collection, entry and analysis. Privacy legislation, limited resources, and incompatible data management systems pose challenges to data sharing and interoperability. Underreporting of missing incidents to police results in an underestimation of missing incidents. An intersectoral, uniform approach to data collection would enable the storage, analysis and comparison of national data. Accurate data on critical wandering can inform prevention, search strategies, resource allocation and effectiveness of programs.

Contact Tracing: Ensuring Privacy and Security

Faced with the biggest virus outbreak in a century, world governments at the start of 2020 took unprecedented measures to protect their healthcare systems from being overwhelmed in the light of the COVID-19 pandemic. International travel was halted and lockdowns were imposed. Many nations adopted measures to stop the transmission of the virus, such as imposing the wearing of face masks, social distancing, and limits on social gatherings. Technology was quickly developed for mobile phones, allowing governments to track people’s movements concerning locations of the virus (both people and places). These are called contact tracing applications. Contact tracing applications raise serious privacy and security concerns. Within Europe, two systems evolved: a centralised system, which calculates risk on a central server, and a decentralised system, which calculates risk on the users’ handset. This study examined both systems from a threat perspective to design a framework that enables privacy and security for contact tracing applications. Such a framework is helpful for App developers. The study found that even though both systems comply with the General Data Protection Regulation (GDPR), Europe’s privacy legislation, the centralised system suffers from severe risks against the threats identified. Experiments, research, and reviews tested the decentralised system in various settings but found that it performs better but still suffers from inherent shortcomings. User tracking and re-identification are possible, especially when users report themselves as infected. Based on these data, the study identified and validated a framework that enables privacy and security. The study also found that the current implementations using the decentralised Google/Apple API do not comply with the framework.

Modern Privacy Threats and Privacy Preservation Techniques in Data Analytics

Today we are living in a digital rich and technology driven world where extremely large amounts of data get generated every hour in the public domain, which also includes personal data. Applications like social media, e-commerce, smartphone apps, etc. collect a lot of personal data which can harm individual privacy if leaked, and hence ethical code of conduct is required to ensure data privacy. Some of the privacy threats include Digital profiling, cyberstalking, recommendation systems, etc. leading to the disclosure of sensitive data and sharing of data without the consent of the data owner. Data Privacy has gained significant importance in the recent times and it is evident from the privacy legislation passed in more than 100 countries. Firms dealing with data-sensitive applications need to abide by the privacy legislation of respective territorial regions. To overcome these privacy challenges by incorporating privacy regulations, we have designed guidelines for application development, incorporating key features of privacy regulations along with the implementation strategies which will help in developing data-sensitive applications which can offer strong and coherent privacy protection of personal data.

AI and privacy concerns: a smart meter case study

Purpose The purpose of this paper is to demonstrate privacy concerns arising from the rapidly increasing advancements and use of artificial intelligence (AI) technology and the challenges of existing privacy regimes to ensure the on-going protection of an individual’s sensitive private information. The authors illustrate this through a case study of energy smart meters and suggest a novel combination of four solutions to strengthen privacy protection. Design/methodology/approach The authors illustrate how, through smart meter obtained energy data, home energy providers can use AI to reveal private consumer information such as households’ electrical appliances, their time and frequency of usage, including number and model of appliance. The authors show how this data can further be combined with other data to infer sensitive personal information such as lifestyle and household income due to advances in AI technologies. Findings The authors highlight data protection and privacy concerns which are not immediately obvious to consumers due to the capabilities of advanced AI technology and its ability to extract sensitive personal information when applied to large overlapping granular data sets. Social implications The authors question the adequacy of existing privacy legislation to protect sensitive inferred consumer data from AI-driven technology. To address this, the authors suggest alternative solutions. Originality/value The original value of this paper is that it illustrates new privacy issues brought about by advances in AI, failings in current privacy legislation and implementation and opens the dialog between stakeholders to protect vulnerable consumers.

Swarm Learning for decentralized and confidential clinical machine learning

Fast and reliable detection of patients with severe and heterogeneous illnesses is a major goal of precision medicine1,2. Patients with leukaemia can be identified using machine learning on the basis of their blood transcriptomes3. However, there is an increasing divide between what is technically possible and what is allowed, because of privacy legislation4,5. Here, to facilitate the integration of any medical data from any data owner worldwide without violating privacy laws, we introduce Swarm Learning—a decentralized machine-learning approach that unites edge computing, blockchain-based peer-to-peer networking and coordination while maintaining confidentiality without the need for a central coordinator, thereby going beyond federated learning. To illustrate the feasibility of using Swarm Learning to develop disease classifiers using distributed data, we chose four use cases of heterogeneous diseases (COVID-19, tuberculosis, leukaemia and lung pathologies). With more than 16,400 blood transcriptomes derived from 127 clinical studies with non-uniform distributions of cases and controls and substantial study biases, as well as more than 95,000 chest X-ray images, we show that Swarm Learning classifiers outperform those developed at individual sites. In addition, Swarm Learning completely fulfils local confidentiality regulations by design. We believe that this approach will notably accelerate the introduction of precision medicine.

Ética archivística, privacidad y protección de datos personales

It analyzes the main codes of ethics for archivists and their principles on privacy and access. It discusses the Code of Ethics and related documents published by the International Council on Archives, the 2020 ICA-IFLA joint statement on Privacy Legislation and Archiving, the Code of Ethics of Catalan Archivists, several Canadian codes of ethics, and several documents by the Society of American Archivists, including the joint statements with the ALA and the ACRL/RBMS. Finally, it presents the European General Data Protection Regulation and the guidance for archives of the European Archives Group. Resumen Se analizan los principales códigos de ética internacionales para archiveros y sus recomendaciones sobre privacidad y acceso. Se discuten los códigos de ética y documentos del Consejo Internacional de Archivos, la declaración conjunta con la Federación Internacional de Asociaciones de Bibliotecarios y Bibliotecas de 2020, el código deontológico de los archiveros catalanes, varios códigos de Canadá y los documentos relacionados de la Sociedad de Archiveros Americanos, incluyendo aquellos presentados de forma conjunta con la ALA y la ACRL/RBMS. Para finalizar se presenta el reglamento general de protección de datos de la Unión Europea y las directrices para archivos del European Archives Group.

Privacy, Ethics, and the Dark Web

This article examines the impact that dark web activities are having on society. Hacking and data breach activities have created serious challenges to cybersecurity leading to new data privacy legislation in Europe and the United States. The dark web is a segment of the web where people employ special browsers that can mask their identity and hide their network activity. Here can be found a wide range of illicit activities that are oftentimes criminal in nature, including sales of stolen documents, the information of others, and other contraband. Companies are actively trying to monitor dark web activities because new legislation requires them to inform authorities if a breach compromising data privacy has occurred; otherwise, they can be penalized. It is argued that as governments act to reign in dark web activities, they must employ an ethical perspective that is grounded in theory to weigh the intentions of darknet actors and their impact. This is due to the fact that some dark web activities such as whistleblowing can actually benefit society.