published_or_final_version / Real Estate and Construction / Master / Master of Science in Construction Project Management

published_or_final_version / Business Administration / Master / Master of Business Administration

published_or_final_version / abstract / toc / Geography / Master / Master of Geographic Information System

published_or_final_version / abstract / Linguistics / Master / Master of Arts

published_or_final_version / abstract / Computer Science / Doctoral / Doctor of Philosophy

published_or_final_version / abstract / Industrial and Manufacturing Systems Engineering / Master / Master of Philosophy

published_or_final_version / Business Administration / Master / Master of Business Administration

As data mining matures as a field and develops more powerful algorithms for discovering and exploiting patterns in data, the amount of data about individuals that is collected and stored continues to rapidly increase. This increase in data heightens concerns that data mining violates individual privacy. The goal of data mining is to derive aggregate conclusions, which should not reveal sensitive information. However, the data-mining algorithms run on databases containing information about individuals which may be sensitive. The goal of privacy-preserving data mining is to provide high-quality aggregate conclusions while protecting the privacy of the constituent individuals. The field of "privacy-preserving data mining" encompasses a wide variety of different techniques and approaches, and considers many different threat and trust models. Some techniques use perturbation, where noise is added (either directly to the database that is the input to the algorithm or to the output of queries) to obscure values of sensitive attributes; some use generalization, where identifying attributes are given less specific values; and some use cryp- tography, where joint computations between multiple parties are performed on encrypted data to hide inputs. Because these approaches are applied to different scenarios with different threat models, their overall e ectiveness and privacy properties are incomparable. In this thesis I take a pragmatic approach to privacy-preserving data mining and attempt to determine which techniques are suitable to real-world problems that a data miner might wish to solve, such as evaluating and learning decision-tree classifiers. I show that popular techniques for sanitizing databases prior to publication either fail to provide any meaningful privacy guarantees, or else degrade the data to the point of having only negligible data-mining utility. Cryptographic techniques for secure multi-party computation are a natural alternative to sanitized data publication, and guarantee the privacy of inputs by performing computations on encrypted data. Because of its heavy reliance on public-key cryptography, it is conventionally thought to be too slow to apply to real-world problems. I show that tailor-made protocols for specific data-mining problems can be made fast enough to run on real-world problems, and I strengthen this claim with empirical runtime analysis using prototype implementations. I also expand the use of secure computation beyond its traditional scope of applying a known algorithm to private inputs by showing how it can be used to e ciently apply a private algorithm, chosen from a specific class of algorithms, to a private input. / text

近年來，許多專家學者廣泛探討偵測稀有疾病的發生率或稱為叢集上的空間或空間對時間的統計方法及模型。這些方法大部分都是處理個別資料或是只能偵測接近圓形的叢集。在這篇論文中，根據Choynowski在1959年所探討的方法，我們進一步提出針對整體資料去偵測非圓形叢集的方法，並且會將此方法與Nagarwalla’s Spatial Scan Statistic做比較。同時，我們會呈現模擬結果中的型一、型二誤差來衡量此方法的可行性。另外，我們也會將此方法實際應用到台灣的癌症死亡資料做探討。 / In recent years, many statistical methods have been proposed for detecting excesses of rare diseases, i.e., clusters, in space or in space-time. Most of these methods deal with case-event or individual-level data and can only detect clusters with shape close to circles. In this study, adapting Choynowski's (1959) idea, a simulation-based approach is proposed to detect non-circular clusters with aggregate or group-level data. The proposed cluster detection method will be used to compare with a frequently used method: Nagarwalla’s Spatial Scan Statistic. Computer simulation is used to illustrate the validity, with respect to Type-I and Type-II errors, of the proposed approach. In addition, the cancer mortality data in Taiwan area are also used as a demonstration of the proposed test.

A method was developed for assessing the vulnerability of groundwater to contamination from contaminant sources at the soil surface, using a numerical groundwater flow model linked to a digital map database. The method was applied using the pcARC/INFO Geographic Information System (GIS) to input, store, and manipulate base maps, resulting in a database of digital maps for the alluvial aquifer system in the Willamette Valley of western Oregon. Digital elevation maps were created by digitizing topographic maps of land surface (1:250,000 scale), water surface, and the base of the Tertiary-Quaternary sedimentary deposits (1:500,000 scales). Soil association and aquifer unit maps digitized from 1:500,000 scale map sheets were also used. Data were extracted from ARC/INFO to the SURFER software package to create a 3-D surface model for each of the digital elevation maps. An ARC/INFO point coverage was then used to store and overlay these surfaces, allowing the creation of maps of depth to water, saturated thickness, and water table gradient. These data became the input to a numerical finite element groundwater flow model. The model solves a dual formulation problem for the potential function and the stream function to calculate the time-of-travel for water to flow from the surface to the water table and laterally for 100 meters as an index of groundwater vulnerability. A cluster analysis is used to condense the data and form a training data set for a multiple regression model. The regression model is fit to the results of the finite element model with an R-squared of greater than 0.96. The simpler regression model is then used for mapping travel times for the entire study area. When properly calibrated against the finite element model and when combined with the digital map database and Geographic Information System (GIS) procedures described, the regression model can be conveniently used to assess the vulnerability of groundwater to contamination over large areas. / Graduation date: 1989