Efficient Algorithms for Fast Integration on Large Data Sets from Multiple Sources

Background Recent large scale deployments of health information technology have created opportunities for the integration of patient medical records with disparate public health, human service, and educational databases to provide comprehensive information related to health and development. Data integration techniques, which identify records belonging to the same individual that reside in multiple data sets, are essential to these efforts. Several algorithms have been proposed in the literatures that are adept in integrating records from two different datasets. Our algorithms are aimed at integrating multiple (in particular more than two) datasets efficiently. Methods Hierarchical clustering based solutions are used to integrate multiple (in particular more than two) datasets. Edit distance is used as the basic distance calculation, while distance calculation of common input errors is also studied. Several techniques have been applied to improve the algorithms in terms of both time and space: 1) Partial Construction of the Dendrogram (PCD) that ignores the level above the threshold; 2) Ignoring the Dendrogram Structure (IDS); 3) Faster Computation of the Edit Distance (FCED) that predicts the distance with the threshold by upper bounds on edit distance; and 4) A pre-processing blocking phase that limits dynamic computation within each block. Results We have experimentally validated our algorithms on large simulated as well as real data. Accuracy and completeness are defined stringently to show the performance of our algorithms. In addition, we employ a four-category analysis. Comparison with FEBRL shows the robustness of our approach. Conclusions In the experiments we conducted, the accuracy we observed exceeded 90% for the simulated data in most cases. 97.7% and 98.1% accuracy were achieved for the constant and proportional threshold, respectively, in a real dataset of 1,083,878 records

Beyond De-Identification Record Falsification to Disarm Expropriated Data-Sets

The wild enthusiasm for big data and open data has brought with it the assumptions that the utility of data-sets is what matters, and that privacy interests are to be sacrificed for the greater good. As a result, techniques have been devised to reduce the identifiability of expropriated data-records, on the assumption that privacy is to be compromised to the extent necessary. This paper argues for and adopts data privacy as the objective, and treats data utility for secondary purposes as the constraint. The inadequacies of both the concept and the implementation of de-identification are underlined. Synthetic data and Known Irreversible Record Falsification (KIRF) are identified as the appropriate techniques to protect against harm arising from expropriated data-sets

Dynamic sorted neighborhood indexing for real-time entity resolution

Real-time Entity Resolution (ER) is the process of matching query records in subsecond time with records in a database that represent the same real-world entity. Indexing techniques are generally used to efficiently extract a set of candidate records from the database that are similar to a query record, and that are to be compared with the query record in more detail. The sorted neighborhood indexing method, which sorts a database and compares records within a sliding window, has been successfully used for ER of large static databases. However, because it is based on static sorted arrays and is designed for batch ER that resolves all records in a database rather than resolving those relating to a single query record, this technique is not suitable for real-time ER on dynamic databases that are constantly updated. We propose a tree-based technique that facilitates dynamic indexing based on the sorted neighborhood method, which can be used for real-time ER, and investigate both static and adaptive window approaches. We propose an approach to reduce query matching times by precalculating the similarities between attribute values stored in neighboring tree nodes. We also propose a multitree solution where different sorting keys are used to reduce the effects of errors and variations in attribute values on matching quality by building several distinct index trees. We experimentally evaluate our proposed techniques on large real datasets, as well as on synthetic data with different data quality characteristics. Our results show that as the index grows, no appreciable increase occurs in both record insertion and query times, and that using multiple trees gives noticeable improvements on matching quality with only a small increase in query time. Compared to earlier indexing techniques for real-time ER, our approach achieves significantly reduced indexing and query matching times while maintaining high matching accuracy

Fraud detection for online banking for scalable and distributed data

Online fraud causes billions of dollars in losses for banks. Therefore, online banking fraud detection is an important field of study. However, there are many challenges in conducting research in fraud detection. One of the constraints is due to unavailability of bank datasets for research or the required characteristics of the attributes of the data are not available. Numeric data usually provides better performance for machine learning algorithms. Most transaction data however have categorical, or nominal features as well. Moreover, some platforms such as Apache Spark only recognizes numeric data. So, there is a need to use techniques e.g. One-hot encoding (OHE) to transform categorical features to numerical features, however OHE has challenges including the sparseness of transformed data and that the distinct values of an attribute are not always known in advance. Efficient feature engineering can improve the algorithm’s performance but usually requires detailed domain knowledge to identify correct features. Techniques like Ripple Down Rules (RDR) are suitable for fraud detection because of their low maintenance and incremental learning features. However, high classification accuracy on mixed datasets, especially for scalable data is challenging. Evaluation of RDR on distributed platforms is also challenging as it is not available on these platforms. The thesis proposes the following solutions to these challenges: • We developed a technique Highly Correlated Rule Based Uniformly Distribution (HCRUD) to generate highly correlated rule-based uniformly-distributed synthetic data. • We developed a technique One-hot Encoded Extended Compact (OHE-EC) to transform categorical features to numeric features by compacting sparse-data even if all distinct values are unknown. • We developed a technique Feature Engineering and Compact Unified Expressions (FECUE) to improve model efficiency through feature engineering where the domain of the data is not known in advance. • A Unified Expression RDR fraud deduction technique (UE-RDR) for Big data has been proposed and evaluated on the Spark platform. Empirical tests were executed on multi-node Hadoop cluster using well-known classifiers on bank data, synthetic bank datasets and publicly available datasets from UCI repository. These evaluations demonstrated substantial improvements in terms of classification accuracy, ruleset compactness and execution speed.Doctor of Philosoph

Scalable and approximate privacy-preserving record linkage

Record linkage, the task of linking multiple databases with the aim to identify records that refer to the same entity, is occurring increasingly in many application areas. Generally, unique entity identifiers are not available in all the databases to be linked. Therefore, record linkage requires the use of personal identifying attributes, such as names and addresses, to identify matching records that need to be reconciled to the same entity. Often, it is not permissible to exchange personal identifying data across different organizations due to privacy and confidentiality concerns or regulations. This has led to the novel research area of privacy-preserving record linkage (PPRL). PPRL addresses the problem of how to link different databases to identify records that correspond to the same real-world entities, without revealing the identities of these entities or any private or confidential information to any party involved in the process, or to any external party, such as a researcher. The three key challenges that a PPRL solution in a real-world context needs to address are (1) scalability to largedatabases by efficiently conducting linkage; (2) achieving high quality of linkage through the use of approximate (string) matching and effective classification of the compared record pairs into matches (i.e. pairs of records that refer to the same entity) and non-matches (i.e. pairs of records that refer to different entities); and (3) provision of sufficient privacy guarantees such that the interested parties only learn the actual values of certain attributes of the records that were classified as matches, and the process is secure with regard to any internal or external adversary. In this thesis, we present extensive research in PPRL, where we have addressed several gaps and problems identified in existing PPRL approaches. First, we begin the thesis with a review of the literature and we propose a taxonomy of PPRL to characterize existing techniques. This allows us to identify gaps and research directions. In the remainder of the thesis, we address several of the identified shortcomings. One main shortcoming we address is a framework for empirical and comparative evaluation of different PPRL solutions, which has not been studied in the literature so far. Second, we propose several novel algorithms for scalable and approximate PPRL by addressing the three main challenges of PPRL. We propose efficient private blocking techniques, for both three-party and two-party scenarios, based on sorted neighborhood clustering to address the scalability challenge. Following, we propose two efficient two-party techniques for private matching and classification to address the linkage quality challenge in terms of approximate matching and effective classification. Privacy is addressed in these approaches using efficient data perturbation techniques including k-anonymous mapping, reference values, and Bloom filters. Finally, the thesis reports on an extensive comparative evaluation of our proposed solutions with several other state-of-the-art techniques on real-world datasets, which shows that our solutions outperform others in terms of all three key challenges

Indexing techniques for real-time entity resolution

Entity resolution (ER), which is the process of identifying records in one or several data set(s) that refer to the same real-world entity, is an important task in improving data quality and in data integration. In general, unique entity identifiers are not available in real-world data sets. Therefore, identifying attributes such as names and addresses are required to perform the ER process using approximate matching techniques. Since many services in both the private and public sectors are moving on-line, organizations increasingly require to perform real-time ER (with sub-second response times) on query records that need to be matched with existing data sets. Indexing is a major step in the ER process which aims to group similar records together using a blocking key criterion to reduce the search space. Most existing indexing techniques that are currently used with ER are static and can only be employed off-line with batch processing algorithms. A major aspect of achieving ER in real-time is to develop novel efficient and effective dynamic indexing techniques that allow dynamic updates and facilitate real-time matching. In this thesis, we focus on the indexing step in the context of real-time ER. We propose three dynamic indexing techniques and a blocking key learning algorithm to be used with real-time ER. The first index (named DySimII) is a blocking-based technique that is updated whenever a new query record arrives. We reduce the size of DySimII by proposing a frequency-filtered alteration that only indexes the most frequent attribute values. The second index (named DySNI) is a tree-based dynamic indexing technique that is tailored for real-time ER. DySNI is based on the sorted neighborhood method that is commonly used in ER. We investigate several static and adaptive window approaches when retrieving candidate records. The third index (named F-DySNI) is a multi-tree technique that uses multiple distinct trees in the index data structure where each tree has a unique sorting key. The aim of F-DySNI is to reduce the effects of errors and variations at the beginning of attribute values that are used as sorting keys on matching quality. Finally, we propose an unsupervised learning algorithm that automatically generates optimal blocking keys for building indexes that are adequate for real-time ER. We experimentally evaluate the proposed approaches using various real-world data sets with millions of records and synthetic data sets with different data characteristics. The results show that, for the growing sizes of our indexing solutions, no appreciable increase occurs in both record insertion and query times. DySNI is the fastest amongst the proposed solutions, while F-DySNI achieves better matching quality. Compared to an existing indexing baseline, our proposed techniques achieve better query times and matching quality. Moreover, our blocking key learning algorithm achieves an average query time that is around two orders of magnitude faster than an existing learning baseline while maintaining similar matching quality. Our proposed solutions are therefore shown to be suitable for real-time ER

Accurate synthetic generation of realistic personal information

Abstract. A large proportion of the massive amounts of data that are being collected by many organisations today is about people, and often contains identifying information like names, addresses, dates of birth, or social security numbers. Privacy and confidentiality are of great concern when such data is being processed and analysed, and when there is a need to share such data between organisations or make it publicly available. The research area of data linkage is especially suffering from a lack of publicly available real-world data sets, as experimental evaluations and comparisons are difficult to conduct without real data. In order to overcome this problem, we have developed a data generator that allows flexible creation of synthetic data with realistic characteristics, such as frequency distributions and error probabilities. Our data generator significantly improves similar earlier approaches, and allows the creation of data containing records for individuals, households and families

Accurate Synthetic Generation of Realistic Personal Information

A large portion of data collected by many organisations today is about people, and often contains personal identifying information, such as names and addresses. Privacy and confidentiality are of great concern when such data is being shared between organisations or made publicly available. Research in (privacy-preserving) data mining and data linkage is suffering from a lack of publicly available real-world data sets that contain personal information, and therefore experimental evaluations can be difficult to conduct. In order to overcome this problem, we have developed a data generator that allows flexible creation of synthetic data containing personal information with realistic characteristics, such as frequency distributions, attribute dependencies, and error probabilities. Our generator significantly improves earlier approaches, and allows the generation of data for individuals, families and households