Quantifying the Costs and Benefits of Privacy-Preserving Health Data Publishing

Cost-benefit analysis is required for making good business decision. This analysis is crucial in the field of privacy-preserving data publishing. In the economic trade of data privacy and utility, organization has the obligation to respect privacy of individuals. They intend to maximize the utility in order to earn revenue and also aim to achieve the acceptable level of privacy. In this thesis, we study the privacy and utility trade-offs and propose an analytical cost model which can help organization in better decision making subject to sharing customer data with another party. We examine the relevant cost factors associated with earning the revenue and the potential damage cost. Our proposed model is suitable for health information custodians (HICs) who share raw patient electronic health records (EHRs) with another health center or health insurer for research and commercial purposes. Health data in its raw form contain significant volume of sensitive data and sharing this data raises issues of privacy breach. Our analytical cost model could be utilized for nonperturbative and perturbative anonymization techniques for relational data. We show that our approach can achieve optimal value as per selection of each privacy model, namely, K-anonymity, LKC-privacy, and ϵ-differential privacy and their anonymization algorithm and level, through extensive experiments on a real-life dataset

Anonymizing and Trading Person-specific Data with Trust

In the past decade, data privacy, security, and trustworthiness have gained tremendous attention from research communities, and these are still active areas of research with the proliferation of cloud services and social media applications. The data is growing at a rapid pace. It has become an integral part of almost every industry and business, including commercial and non-profit organizations. It often contains person-specific information and a data custodian who holds it must be responsible for managing its use, disclosure, accuracy and privacy protection. In this thesis, we present three research problems. The first two problems address the concerns of stakeholders on privacy protection, data trustworthiness, and profit distribution in the online market for trading person-specific data. The third problem addresses the health information custodians (HICs) concern on privacy-preserving healthcare network data publishing. Our first research problem is identified in cloud-based data integration service where data providers collaborate with their trading partners in order to deliver quality data mining services. Data-as-a-Service (DaaS) enables data integration to serve the demands of data consumers. Data providers face challenges not only to protect private data over the cloud but also to legally adhere to privacy compliance rules when trading person-specific data. We propose a model that allows the collaboration of multiple data providers for integrating their data and derives the contribution of each data provider by valuating the incorporated cost factors. This model serves as a guide for business decision-making, such as estimating the potential privacy risk and finding the sub-optimal value for publishing mashup data. Experiments on real-life data demonstrate that our approach can identify the sub-optimal value in data mashup for different privacy models, including K-anonymity, LKC-privacy, and ϵ-differential privacy, with various anonymization algorithms and privacy parameters. Second, consumers demand a good quality of data for accurate analysis and effective decision- making while the data providers intend to maximize their profits by competing with peer providers. In addition, the data providers or custodians must conform to privacy policies to avoid potential penalties for privacy breaches. To address these challenges, we propose a two-fold solution: (1) we present the first information entropy-based trust computation algorithm, IEB_Trust, that allows a semi-trusted arbitrator to detect the covert behavior of a dishonest data provider and chooses the qualified providers for a data mashup, and (2) we incorporate the Vickrey-Clarke-Groves (VCG) auction mechanism for the valuation of data providers’ attributes into the data mashup process. Experiments on real-life data demonstrate the robustness of our approach in restricting dishonest providers from participation in the data mashup and improving the efficiency in comparison to provenance-based approaches. Furthermore, we derive the monetary shares for the chosen providers from their information utility and trust scores over the differentially private release of the integrated dataset under their joint privacy requirements. Finally, we address the concerns of HICs of exchanging healthcare data to provide better and more timely services while mitigating the risk of exposing patients’ sensitive information to privacy threats. We first model a complex healthcare dataset using a heterogeneous information network that consists of multi-type entities and their relationships. We then propose DiffHetNet, an edge-based differentially private algorithm, to protect the sensitive links of patients from inbound and outbound attacks in the heterogeneous health network. We evaluate the performance of our proposed method in terms of information utility and efficiency on different types of real-life datasets that can be modeled as networks. Experimental results suggest that DiffHetNet generally yields less information loss and is significantly more efficient in terms of runtime in comparison with existing network anonymization methods. Furthermore, DiffHetNet is scalable to large network datasets

Enabling Secure Trustworthiness Assessment and Privacy Protection in Integrating Data for Trading Person-Specific Information

IEEE With increasing adoption of cloud services in the e-market, collaboration between stakeholders is easier than ever. Consumer stakeholders demand data from various sources to analyze trends and improve customer services. Data-as-a-service enables data integration to serve the demands of data consumers. However, the data must be of good quality and trustful for accurate analysis and effective decision making. In addition, a data custodian or provider must conform to privacy policies to avoid potential penalties for privacy breaches. To address these challenges, we propose a twofold solution: 1) we present the first information entropy-based trust computation algorithm, IEB_Trust, that allows a semitrusted arbitrator to detect the covert behavior of a dishonest data provider and chooses the qualified providers for a data mashup and 2) we incorporate the Vickrey–Clarke–Groves (VCG) auction mechanism for the valuation of data providers’ attributes into the data mashup process. Experiments on real-life data demonstrate the robustness of our approach in restricting dishonest providers from participation in the data mashup and improving the efficiency in comparison to provenance-based approaches. Furthermore, we derive the monetary shares for the chosen providers from their information utility and trust scores over the differentially private release of the integrated dataset under their joint privacy requirements

Differentially Private Release of Heterogeneous Network for Managing Healthcare Data

With the increasing adoption of digital health platforms through mobile apps and online services, people have greater flexibility connecting with medical practitioners, pharmacists, and laboratories and accessing resources to manage their own health-related concerns. Many healthcare institutions are connecting with each other to facilitate the exchange of healthcare data, with the goal of effective healthcare data management. The contents generated over these platforms are often shared with third parties for a variety of purposes. However, sharing healthcare data comes with the potential risk of exposing patients’ sensitive information to privacy threats. In this article we address the challenge of sharing healthcare data while protecting patients’ privacy. We first model a complex healthcare dataset using a heterogeneous information network that consists of multi-type entities and their relationships. We then propose DiffHetNet , an edge-based differentially private algorithm, to protect the sensitive links of patients from inbound and outbound attacks in the heterogeneous health network. We evaluate the performance of our proposed method in terms of information utility and efficiency on different types of real-life datasets that can be modeled as networks. Experimental results suggest that DiffHetNet generally yields less information loss and is significantly more efficient in terms of runtime in comparison with existing network anonymization methods. Furthermore, DiffHetNet is scalable to large network datasets

Privacy-preserving data mashup model for trading person-specific information

© 2016 Elsevier B.V. All rights reserved. Business enterprises adopt cloud integration services to improve collaboration with their trading partners and to deliver quality data mining services. Data-as-a-Service (DaaS) mashup allows multiple enterprises to integrate their data upon the demand of consumers. Business enterprises face challenges not only to protect private data over the cloud but also to legally adhere to privacy compliance rules when trading person-specific data. They need an effective privacy-preserving business model to deal with the challenges in emerging markets. We propose a model that allows the collaboration of multiple enterprises for integrating their data and derives the contribution of each data provider by valuating the incorporated cost factors. This model serves as a guide for business decision-making, such as estimating the potential risk and finding the optimal value for publishing mashup data. Experiments on real-life data demonstrate that our approach can identify the optimal value in data mashup for different privacy models, including K-anonymity, LKC-privacy, and ∈-differential privacy, with various anonymization algorithms and privacy parameters

The impact of surgical delay on resectability of colorectal cancer: An international prospective cohort study

AIM: The SARS-CoV-2 pandemic has provided a unique opportunity to explore the impact of surgical delays on cancer resectability. This study aimed to compare resectability for colorectal cancer patients undergoing delayed versus non-delayed surgery. METHODS: This was an international prospective cohort study of consecutive colorectal cancer patients with a decision for curative surgery (January-April 2020). Surgical delay was defined as an operation taking place more than 4 weeks after treatment decision, in a patient who did not receive neoadjuvant therapy. A subgroup analysis explored the effects of delay in elective patients only. The impact of longer delays was explored in a sensitivity analysis. The primary outcome was complete resection, defined as curative resection with an R0 margin. RESULTS: Overall, 5453 patients from 304 hospitals in 47 countries were included, of whom 6.6% (358/5453) did not receive their planned operation. Of the 4304 operated patients without neoadjuvant therapy, 40.5% (1744/4304) were delayed beyond 4 weeks. Delayed patients were more likely to be older, men, more comorbid, have higher body mass index and have rectal cancer and early stage disease. Delayed patients had higher unadjusted rates of complete resection (93.7% vs. 91.9%, P = 0.032) and lower rates of emergency surgery (4.5% vs. 22.5%, P < 0.001). After adjustment, delay was not associated with a lower rate of complete resection (OR 1.18, 95% CI 0.90-1.55, P = 0.224), which was consistent in elective patients only (OR 0.94, 95% CI 0.69-1.27, P = 0.672). Longer delays were not associated with poorer outcomes. CONCLUSION: One in 15 colorectal cancer patients did not receive their planned operation during the first wave of COVID-19. Surgical delay did not appear to compromise resectability, raising the hypothesis that any reduction in long-term survival attributable to delays is likely to be due to micro-metastatic disease

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Anesthetic management of patient with hemophilia a undergoing emergency ventriculoperitoneal shunting: A case report and review of literature

Hemophilia A is a hemorrhagic trend almost exclusively affecting males (X-related recessive disease). In 85% of cases, it is caused by factor VIII deficiency, called hemophilia A or classic hemophilia. Successful anesthetic management depends on the special care and a multidisciplinary team of health professionals informed about the disease, including qualified hematologist, surgeon, and anesthesiologist

Anesthetic management of patient with hemophilia a undergoing emergency ventriculoperitoneal shunting: A case report and review of literature

Hemophilia A is a hemorrhagic trend almost exclusively affecting males (X-related recessive disease). In 85% of cases, it is caused by factor VIII deficiency, called hemophilia A or classic hemophilia. Successful anesthetic management depends on the special care and a multidisciplinary team of health professionals informed about the disease, including qualified hematologist, surgeon, and anesthesiologist