Is blockchain the breakthrough we are looking for to facilitate genomic data sharing? The European Union perspective

The recent progress of genomics research is providing unprecedented insight into human genetic variance, susceptibility to disease and risk stratification. Current trends predict that a massive amount of genomic data will be produced in the upcoming years which, when coupled with the fast-paced development of the field, will create new social, ethical, and legal challenges. In the complex legislative environment of the European Union, genomic data sharing policies will have to weigh the benefits of scientific discovery against the ethical risks posed by the act of sharing sensitive data. In this complex, interconnected environment, blockchain provides a unique and novel solution to accountability, traceability, and transparency issues regarding genomic data sharing. Implementing a distributed ledger technology-based database could empower both patients and citizens to responsibly use genomic data pertaining to them because it allows for a higher degree of control over the recipients of their data and their uses. The blockchain technology will engage both data owners and policymakers to address the multiple issues of genomic data sharing and allow us to redefine the way we look at genomics

An Evaluation of Overseas Oil Investment Projects under Uncertainty Using a Real Options Based Simulation Model

This paper applies real options theory to establish an overseas oil investment evaluation model that is based on Monte Carlo simulation and is solved by the Least Squares Monte-Carlo method. To better reflect the reality of overseas oil investment, our model has incorporated not only the uncertainties of oil price and investment cost but also the uncertainties of exchange rate and investment environment. These unique features have enabled our model to be best equipped to evaluate the value of oil overseas investment projects of three oil field sizes (large, medium, small) and under different resource tax systems (royalty tax and production sharing contracts). In our empirical setting, we have selected China as an investor country and Indonesia as an investee country as a case study. Our results show that the investment risks and project values of small sized oil fields are more sensitive to changes in the uncertainty factors than the large and medium sized oil fields. Furthermore, among the uncertainty factors considered in the model, the investment risk of overseas oil investment may be underestimated if no consideration is given of the impacts of exchange rate and investment environment. Finally, as there is an important trade-off between oil resource investee country and overseas oil investor, in medium and small sized oil investment negotiation the oil company should try to increase the cost oil limit in production sharing contract and avoid the term of a windfall profits tax to reduce the investment risk of overseas oil fields.Overseas Oil Investment, Project Value, Real Options, Least Squares Monte-Carlo

LACE: Supporting Privacy-Preserving Data Sharing in Transfer Defect Learning

Cross Project Defect Prediction (CPDP) is a field of study where an organization lacking enough local data can use data from other organizations or projects for building defect predictors. Research in CPDP has shown challenges in using ``other\u27\u27 data, therefore transfer defect learning has emerged to improve on the quality of CPDP results. With this new found success in CPDP, it is now increasingly important to focus on the privacy concerns of data owners.;To support CPDP, data must be shared. There are many privacy threats that inhibit data sharing. We focus on sensitive attribute disclosure threats or attacks, where an attacker seeks to associate a record(s) in a data set to its sensitive information. Solutions to this sharing problem comes from the field of Privacy Preserving Data Publishing (PPDP) which has emerged as a means to confuse the efforts of sensitive attribute disclosure attacks and therefore reduce privacy concerns. PPDP covers methods and tools used to disguise raw data for publishing. However, prior work warned that increasing data privacy decreases the efficacy of data mining on privatized data.;The goal of this research is to help encourage organizations and individuals to share their data publicly and/or with each other for research purposes and/or improving the quality of their software product through defect prediction. The contributions of this work allow three benefits for data owners willing to share privatized data: 1) that they are fully aware of the sensitive attribute disclosure risks involved so they can make an informed decision about what to share, 2) they are provided with the ability to privatize their data and have it remain useful, and 3) the ability to work with others to share their data based on what they learn from each others data. We call this private multiparty data sharing.;To achieve these benefits, this dissertation presents LACE (Large-scale Assurance of Confidentiality Environment). LACE incorporates a privacy metric called IPR (Increased Privacy Ratio) which calculates the risk of sensitive attribute disclosure of data through comparing results of queries (attacks) on the original data and a privatized version of that data. LACE also includes a privacy algorithm which uses intelligent instance selection to prune the data to as low as 10% of the original data (thus offering complete privacy to the other 90%). It then mutates the remaining data making it possible that over 70% of sensitive attribute disclosure attacks are unsuccessful. Finally, LACE can facilitate private multiparty data sharing via a unique leader-follower algorithm (developed for this dissertation). The algorithm allows data owners to serially build a privatized data set, by allowing them to only contribute data that are not already in the private cache. In this scenario, each data owner shares even less of their data, some as low as 2%.;The experiments of this thesis, lead to the following conclusion: at least for the defect data studied here, data can be minimized, privatized and shared without a significant degradation in utility. Specifically, in comparative studies with standard privacy models (k-anonymity and data swapping), applied to 10 open-source data sets and 3 proprietary data sets, LACE produces privatized data sets that are significantly smaller than the original data (as low as 2%). As a result LACE offers better protection against sensitive attribute disclosure attacks than other methods

COINSTAC: A Privacy Enabled Model and Prototype for Leveraging and Processing Decentralized Brain Imaging Data

The field of neuroimaging has embraced the need for sharing and collaboration. Data sharing mandates from public funding agencies and major journal publishers have spurred the development of data repositories and neuroinformatics consortia. However, efficient and effective data sharing still faces several hurdles. For example, open data sharing is on the rise but is not suitable for sensitive data that are not easily shared, such as genetics. Current approaches can be cumbersome (such as negotiating multiple data sharing agreements). There are also significant data transfer, organization and computational challenges. Centralized repositories only partially address the issues. We propose a dynamic, decentralized platform for large scale analyses called the Collaborative Informatics and Neuroimaging Suite Toolkit for Anonymous Computation (COINSTAC). The COINSTAC solution can include data missing from central repositories, allows pooling of both open and ``closed'' repositories by developing privacy-preserving versions of widely-used algorithms, and incorporates the tools within an easy-to-use platform enabling distributed computation. We present an initial prototype system which we demonstrate on two multi-site data sets, without aggregating the data. In addition, by iterating across sites, the COINSTAC model enables meta-analytic solutions to converge to ``pooled-data'' solutions (i.e. as if the entire data were in hand). More advanced approaches such as feature generation, matrix factorization models, and preprocessing can be incorporated into such a model. In sum, COINSTAC enables access to the many currently unavailable data sets, a user friendly privacy enabled interface for decentralized analysis, and a powerful solution that complements existing data sharing solutions

Investigation of charge sharing among electrode strips for a CdZnTe detector

We have investigated charge sharing among the anode strips of a CdZnTe (CZT) detector using a 30 micrometer collimated gamma-ray beam. We compared the laboratory measurements with the predictions from our modeling of the charge transport within the detector. The results indicate that charge sharing is a function of the interaction depth and the energy of the incoming photon. Also, depending on depth, a fraction of the electrons might drift to the inter-anode region causing incomplete charge collection. Here, we show that photoelectron range and diffusion of the charge cloud are the principal causes of charge sharing and obtain limits on the size of the electron cloud as a function of position in the detector.Comment: 16 pages 10 figures. Accepted for publication in Nuclear Instruments and Methods -

Routes for breaching and protecting genetic privacy

We are entering the era of ubiquitous genetic information for research, clinical care, and personal curiosity. Sharing these datasets is vital for rapid progress in understanding the genetic basis of human diseases. However, one growing concern is the ability to protect the genetic privacy of the data originators. Here, we technically map threats to genetic privacy and discuss potential mitigation strategies for privacy-preserving dissemination of genetic data.Comment: Draft for comment