Valuing Uncertainties in Wind Generation: An Agent-Based Optimization Approach

The increasing integration of variable renewable energy sources such as wind and solar will require new methods of managing generation uncertainty. Existing practices of uncertainty management for these resources largely focuses around modifying the energy offers of such resources in the quantity domain and from a centralized system operator consideration of these uncertainties. This paper proposes an approach to instead consider these uncertainties in the price domain, where more uncertain power is offered at a higher price instead of restricting the quantity offered. We demonstrate system-level impacts on a modified version of the RTS-GMLC system where wind generators create market offers valuing their uncertainties over scenario set of day-ahead production forecasts. The results are compared with a dispatch method in which wind energy is offered at zero marginal price and restricted based on the forecast percentile.Comment: 6 pages, 3 figures. Submitted to the 2023 American Control Conference (ACC

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

An Overview of Public Health in the New Millenium: Individual Liberty vs. Public Safety

This article explores the tensions between creating an effective public health system that would be able to respond to and protect against any public health threat, and protecting individuals against unnecessary intrusions on their civil liberties. It then considers approaches to this issue that might best strike a balance in a democratic society. While many Americans may recognize and even accept that greater security would entail some intrusion into individual rights, there is no formula for striking the appropriate balance. This article attempts to arrive at a workable framework by examining how the United States\u27 public health system works. This includes reviewing its policy response to several recent public health threats, exposing the shortcomings of the current system, and comparing it to the approach of other democratic and non-democratic societies. based upon this review and analysis, the article suggests an approach that might best incorporate effective techniques from a variety of alternative systems, while addressing some of the main problems of the current framework. This analysis is broken down into seven main parts. Part I provides an introduction to public health and the essential components of an effective system. It explains why public health historically has not been high on the priority list of medical approaches to combating disease, and describes how this view of public health has evolved, particularly in recent years. Part II examines some of the shortcomings of the United States\u27 public health system as it currently stands. Part III introduces the central controversy between civil liberties and a strong public health system by focusing on three of the most commonly used tools of public health authorities, namely: quarantines, mandatory screening and immunization, and health information sharing as well as their effects on liberty and freedom of movement, individual autonomy, and privacy. Part IV builds on this analysis and explores how the United States historically has struck a balance between these competing considerations by examining orders and legislation arising out of recent public health threats such as AIDS, SARS, and 9/11. Part V investigates other approaches to resolving the tensions between public health and civil liberties by reviewing the approach advocated by one renown public health expert, Lawrence Gostin, and the response of Canadian and Singaporean societies to the threat of SARS. Part VI continues to explore alternatives, focusing solely on the Model State Emergency Health Powers Act ( MSEHPA ), developed in response to 9/11. Finally, Part VII identifies the shortcomings of MSEHPA and recommends amendments to MSEHPA that might help to strike a better balance for the American people. Addressing the criticisms and concerns voiced by the American public is an essential step toward creating a viable, stronger public health system going forward

Peer-To-Peer Backup for Personal Area Networks

FlashBack is a peer-to-peer backup algorithm designed for power-constrained devices running in a personal area network (PAN). Backups are performed transparently as local updates initiate the spread of backup data among a subset of the currently available peers. Flashback limits power usage by avoiding flooding and keeping small neighbor sets. Flashback has also been designed to utilize powered infrastructure when possible to further extend device lifetime. We propose our architecture and algorithms, and present initial experimental results that illustrate FlashBack’s performance characteristic