The microdata analysis system at the U.S. Census Bureau

The U.S. Census Bureau has the responsibility to release high quality data products while maintaining the confidentiality promised to all respondents under Title 13 of the U.S. Code. This paper describes a Microdata Analysis System (MAS) that is currently under development, which will allow users to receive certain statistical analyses of Census Bureau data, such as crosstabulations and regressions, without ever having access to the data themselves. Such analyses must satisfy several statistical confidentiality rules; those that fail these rules will not be output to the user. In addition, the Drop q Rule, which requires removing a relatively small number of units before performing an analysis, is applied to all datasets. We describe the confidentiality rules and briefly outline an evaluation of the effectiveness of the Drop q Rule. We conclude with a description of other approaches to creating a system of this sort, and some directions for future research

Is There a Market for Work Group Servers? Evaluating Market Level Demand Elasticities Using Micro and Macro Models

This paper contains an empirical analysis demand for "work-group" (or low-end) servers. Servers are at thecentre of many US and EU anti-trust debates, including the Hewlett-Packard/Compaq merger and investigationsinto the activities of Microsoft. One question in these policy decisions is whether a high share of work serversindicates anything about shortrun market power. To investigate price elasticities we use model-level panel dataon transaction prices, sales and characteristics of practically every server in the world. We contrast estimatesfrom the traditional "macro" approaches that aggregate across brands and modern "micro" approaches that usebrand-level information (including both "distance metric" and logit based approaches). We find that the macroapproaches lead to overestimates of consumer price sensitivity. Our preferred micro-based estimates of themarket level elasticity of demand for work group servers are around 0.3 to 0.6 (compared to 1 to 1.3 in themacro estimates). Even at the higher range of the estimates, however, we find that demand elasticities aresufficiently low to imply a distinct "anti-trust" market for work group servers and their operating systems. It isunsurprising that firms with large shares of work group servers have come under some antitrust scrutiny.demand elasticities, network servers, computers, anti-trust

Split Leverage: Attacking the Condentiality of Linked Databases by Partitioning

This paper considers the risk of disclosure in linked databases when statistical analysis of micro-data is permitted. The risk of dis- closure needs to be balanced against the utility of the linked data. The current work specifically considers the disclosure risks in permit- ting regression analysis to be performed on linked data. A new attack based on partitioning of the database is presented

Investigating the Impacts of Cloud Computing on Firm Profitability

The advent of cloud computing has been a technical revolution that transformed how organizations access, store, and process information. This research proposes that cloud deployment can have a significant impact on profitability in multiple ways. We argued that one of the most significant ways is by reducing costs by eliminating the need for businesses to invest in and maintain their own IT infrastructure, making it easier for businesses to scale their resources up or down as needed, improving agility, and providing advanced security features and tools. Additionally, cloud deployment can increase profitability through increased scalability, improved collaboration, access to new technologies such as machine learning and big data, and improved customer experience by providing faster and more reliable service. By implementing cloud deployment, businesses can also increase revenue and improve overall operational efficiency and productivity. Using the datasets of 115 firms, this research investigated the impact of various cloud-use matrices on firm profitability. The results indicate that the gross profit margins of firms are increased when services delivered via the cloud, cloud spending, best cloud governance, and the number of cloud-based applications are increased in a more concentrated market with less competition. To increase the positive impact of cloud computing on a business organization, it is important to develop a clear and comprehensive cloud strategy, establish robust security and compliance policies, invest in the necessary resources and expertise for successful cloud migration, and continuously monitor and measure the performance and effectiveness of the cloud solutions. This will help organizations make informed decisions, align their cloud investments with their overall business goals and objectives, mitigate security and compliance risks, ensure a successful cloud migration, and continuously optimize their cloud solutions for maximum value. By taking this holistic approach, businesses can ensure that they get the most value out of their cloud investments and achieve optimal results

Machine learning solutions for maintenance of power plants

The primary goal of this work is to present analysis of current market for predictive maintenance software solutions applicable to a generic coal/gas-fired thermal power plant, as well as to present a brief discussion on the related developments of the near future. This type of solutions is in essence an advanced condition monitoring technique, that is used to continuously monitor entire plants and detect sensor reading deviations via correlative calculations. This approach allows for malfunction forecasting well in advance to a malfunction itself and any possible unforeseen consequences. Predictive maintenance software solutions employ primitive artificial intelligence in the form of machine learning (ML) algorithms to provide early detection of signal deviation. Before analyzing existing ML based solutions, structure and theory behind the processes of coal/gas driven power plants is going to be discussed to emphasize the necessity of predictive maintenance for optimal and reliable operation. Subjects to be discussed are: basic theory (thermodynamics and electrodynamics), primary machinery types, automation systems and data transmission, typical faults and condition monitoring techniques that are also often used in tandem with ML. Additionally, the basic theory on the main machine learning techniques related to malfunction prediction is going to be briefly presented