Privacy preserving association rule mining using attribute-identity mapping

Association rule mining uncovers hidden yet important patterns in data. Discovery of the patterns helps data owners to make right decision to enhance efficiency, increase profit and reduce loss. However, there is privacy concern especially when the data owner is not the miner or when many parties are involved. This research studied privacy preserving association rule mining (PPARM) of horizontally partitioned and outsourced data. Existing research works in the area concentrated mainly on the privacy issue and paid very little attention to data quality issue. Meanwhile, the more the data quality, the more accurate and reliable will the association rules be. Consequently, this research proposed Attribute-Identity Mapping (AIM) as a PPARM technique to address the data quality issue. Given a dataset, AIM identifies set of attributes, attribute values for each attribute. It then assigns ‘unique’ identity for each of the attributes and their corresponding values. It then generates sanitized dataset by replacing each attribute and its values with their corresponding identities. For privacy preservation purpose, the sanitization process will be carried out by data owners. They then send the sanitized data, which is made up of only identities, to data miner. When any or all the data owners need(s) ARM result from the aggregate data, they send query to the data miner. The query constitutes attributes (in form of identities), minSup and minConf thresholds and then number of rules they are want. Results obtained show that the PPARM technique maintains 100% data quality without compromising privacy, using Census Income dataset

Similarity processing in multi-observation data

Many real-world application domains such as sensor-monitoring systems for environmental research or medical diagnostic systems are dealing with data that is represented by multiple observations. In contrast to single-observation data, where each object is assigned to exactly one occurrence, multi-observation data is based on several occurrences that are subject to two key properties: temporal variability and uncertainty. When defining similarity between data objects, these properties play a significant role. In general, methods designed for single-observation data hardly apply for multi-observation data, as they are either not supported by the data models or do not provide sufficiently efficient or effective solutions. Prominent directions incorporating the key properties are the fields of time series, where data is created by temporally successive observations, and uncertain data, where observations are mutually exclusive. This thesis provides research contributions for similarity processing - similarity search and data mining - on time series and uncertain data. The first part of this thesis focuses on similarity processing in time series databases. A variety of similarity measures have recently been proposed that support similarity processing w.r.t. various aspects. In particular, this part deals with time series that consist of periodic occurrences of patterns. Examining an application scenario from the medical domain, a solution for activity recognition is presented. Finally, the extraction of feature vectors allows the application of spatial index structures, which support the acceleration of search and mining tasks resulting in a significant efficiency gain. As feature vectors are potentially of high dimensionality, this part introduces indexing approaches for the high-dimensional space for the full-dimensional case as well as for arbitrary subspaces. The second part of this thesis focuses on similarity processing in probabilistic databases. The presence of uncertainty is inherent in many applications dealing with data collected by sensing devices. Often, the collected information is noisy or incomplete due to measurement or transmission errors. Furthermore, data may be rendered uncertain due to privacy-preserving issues with the presence of confidential information. This creates a number of challenges in terms of effectively and efficiently querying and mining uncertain data. Existing work in this field either neglects the presence of dependencies or provides only approximate results while applying methods designed for certain data. Other approaches dealing with uncertain data are not able to provide efficient solutions. This part presents query processing approaches that outperform existing solutions of probabilistic similarity ranking. This part finally leads to the application of the introduced techniques to data mining tasks, such as the prominent problem of probabilistic frequent itemset mining.Viele Anwendungsgebiete, wie beispielsweise die Umweltforschung oder die medizinische Diagnostik, nutzen Systeme der Sensorüberwachung. Solche Systeme müssen oftmals in der Lage sein, mit Daten umzugehen, welche durch mehrere Beobachtungen repräsentiert werden. Im Gegensatz zu Daten mit nur einer Beobachtung (Single-Observation Data) basieren Daten aus mehreren Beobachtungen (Multi-Observation Data) auf einer Vielzahl von Beobachtungen, welche zwei Schlüsseleigenschaften unterliegen: Zeitliche Veränderlichkeit und Datenunsicherheit. Im Bereich der Ähnlichkeitssuche und im Data Mining spielen diese Eigenschaften eine wichtige Rolle. Gängige Lösungen in diesen Bereichen, die für Single-Observation Data entwickelt wurden, sind in der Regel für den Umgang mit mehreren Beobachtungen pro Objekt nicht anwendbar. Der Grund dafür liegt darin, dass diese Ansätze entweder nicht mit den Datenmodellen vereinbar sind oder keine Lösungen anbieten, die den aktuellen Ansprüchen an Lösungsqualität oder Effizienz genügen. Bekannte Forschungsrichtungen, die sich mit Multi-Observation Data und deren Schlüsseleigenschaften beschäftigen, sind die Analyse von Zeitreihen und die Ähnlichkeitssuche in probabilistischen Datenbanken. Während erstere Richtung eine zeitliche Ordnung der Beobachtungen eines Objekts voraussetzt, basieren unsichere Datenobjekte auf Beobachtungen, die sich gegenseitig bedingen oder ausschließen. Diese Dissertation umfasst aktuelle Forschungsbeiträge aus den beiden genannten Bereichen, wobei Methoden zur Ähnlichkeitssuche und zur Anwendung im Data Mining vorgestellt werden. Der erste Teil dieser Arbeit beschäftigt sich mit Ähnlichkeitssuche und Data Mining in Zeitreihendatenbanken. Insbesondere werden Zeitreihen betrachtet, welche aus periodisch auftretenden Mustern bestehen. Im Kontext eines medizinischen Anwendungsszenarios wird ein Ansatz zur Aktivitätserkennung vorgestellt. Dieser erlaubt mittels Merkmalsextraktion eine effiziente Speicherung und Analyse mit Hilfe von räumlichen Indexstrukturen. Für den Fall hochdimensionaler Merkmalsvektoren stellt dieser Teil zwei Indexierungsmethoden zur Beschleunigung von ähnlichkeitsanfragen vor. Die erste Methode berücksichtigt alle Attribute der Merkmalsvektoren, während die zweite Methode eine Projektion der Anfrage auf eine benutzerdefinierten Unterraum des Vektorraums erlaubt. Im zweiten Teil dieser Arbeit wird die Ähnlichkeitssuche im Kontext probabilistischer Datenbanken behandelt. Daten aus Sensormessungen besitzen häufig Eigenschaften, die einer gewissen Unsicherheit unterliegen. Aufgrund von Mess- oder übertragungsfehlern sind gemessene Werte oftmals unvollständig oder mit Rauschen behaftet. In diversen Szenarien, wie beispielsweise mit persönlichen oder medizinisch vertraulichen Daten, können Daten auch nachträglich von Hand verrauscht werden, so dass eine genaue Rekonstruktion der ursprünglichen Informationen nicht möglich ist. Diese Gegebenheiten stellen Anfragetechniken und Methoden des Data Mining vor einige Herausforderungen. In bestehenden Forschungsarbeiten aus dem Bereich der unsicheren Datenbanken werden diverse Probleme oftmals nicht beachtet. Entweder wird die Präsenz von Abhängigkeiten ignoriert, oder es werden lediglich approximative Lösungen angeboten, welche die Anwendung von Methoden für sichere Daten erlaubt. Andere Ansätze berechnen genaue Lösungen, liefern die Antworten aber nicht in annehmbarer Laufzeit zurück. Dieser Teil der Arbeit präsentiert effiziente Methoden zur Beantwortung von Ähnlichkeitsanfragen, welche die Ergebnisse absteigend nach ihrer Relevanz, also eine Rangliste der Ergebnisse, zurückliefern. Die angewandten Techniken werden schließlich auf Problemstellungen im probabilistischen Data Mining übertragen, um beispielsweise das Problem des Frequent Itemset Mining unter Berücksichtigung des vollen Gehalts an Unsicherheitsinformation zu lösen

Data mining techniques for complex application domains

The emergence of advanced communication techniques has increased availability of large collection of data in electronic form in a number of application domains including healthcare, e- business, and e-learning. Everyday a large amount of records are stored electronically. However, finding useful information from such a large data collection is a challenging issue. Data mining technology aims automatically extracting hidden knowledge from large data repositories exploiting sophisticated algorithms. The hidden knowledge in the electronic data may be potentially utilized to facilitate the procedures, productivity, and reliability of several application domains. The PhD activity has been focused on novel and effective data mining approaches to tackle the complex data coming from two main application domains: Healthcare data analysis and Textual data analysis. The research activity, in the context of healthcare data, addressed the application of different data mining techniques to discover valuable knowledge from real exam-log data of patients. In particular, efforts have been devoted to the extraction of medical pathways, which can be exploited to analyze the actual treatments followed by patients. The derived knowledge not only provides useful information to deal with the treatment procedures but may also play an important role in future predictions of potential patient risks associated with medical treatments. The research effort in textual data analysis is twofold. On the one hand, a novel approach to discovery of succinct summaries of large document collections has been proposed. On the other hand, the suitability of an established descriptive data mining to support domain experts in making decisions has been investigated. Both research activities are focused on adopting widely exploratory data mining techniques to textual data analysis, which require overcoming intrinsic limitations for traditional algorithms for handling textual documents efficiently and effectively

Comprehensive survey on big data privacy protection

In recent years, the ever-mounting problem of Internet phishing has been threatening the secure propagation of sensitive data over the web, thereby resulting in either outright decline of data distribution or inaccurate data distribution from several data providers. Therefore, user privacy has evolved into a critical issue in various data mining operations. User privacy has turned out to be a foremost criterion for allowing the transfer of confidential information. The intense surge in storing the personal data of customers (i.e., big data) has resulted in a new research area, which is referred to as privacy-preserving data mining (PPDM). A key issue of PPDM is how to manipulate data using a specific approach to enable the development of a good data mining model on modified data, thereby meeting a specified privacy need with minimum loss of information for the intended data analysis task. The current review study aims to utilize the tasks of data mining operations without risking the security of individuals’ sensitive information, particularly at the record level. To this end, PPDM techniques are reviewed and classified using various approaches for data modification. Furthermore, a critical comparative analysis is performed for the advantages and drawbacks of PPDM techniques. This review study also elaborates on the existing challenges and unresolved issues in PPDM.Published versio

New Fundamental Technologies in Data Mining

The progress of data mining technology and large public popularity establish a need for a comprehensive text on the subject. The series of books entitled by "Data Mining" address the need by presenting in-depth description of novel mining algorithms and many useful applications. In addition to understanding each section deeply, the two books present useful hints and strategies to solving problems in the following chapters. The contributing authors have highlighted many future research directions that will foster multi-disciplinary collaborations and hence will lead to significant development in the field of data mining

Abduction and Anonymity in Data Mining

This thesis investigates two new research problems that arise in modern data mining: reasoning on data mining results, and privacy implication of data mining results. Most of the data mining algorithms rely on inductive techniques, trying to infer information that is generalized from the input data. But very often this inductive step on raw data is not enough to answer the user questions, and there is the need to process data again using other inference methods. In order to answer high level user needs such as explanation of results, we describe an environment able to perform abductive (hypothetical) reasoning, since often the solutions of such queries can be seen as the set of hypothesis that satisfy some requirements. By using cost-based abduction, we show how classification algorithms can be boosted by performing abductive reasoning over the data mining results, improving the quality of the output. Another growing research area in data mining is the one of privacy-preserving data mining. Due to the availability of large amounts of data, easily collected and stored via computer systems, new applications are emerging, but unfortunately privacy concerns make data mining unsuitable. We study the privacy implications of data mining in a mathematical and logical context, focusing on the anonymity of people whose data are analyzed. A formal theory on anonymity preserving data mining is given, together with a number of anonymity-preserving algorithms for pattern mining. The post-processing improvement on data mining results (w.r.t. utility and privacy) is the central focus of the problems we investigated in this thesis

Analytical study and computational modeling of statistical methods for data mining

Today, there is tremendous increase of the information available on electronic form. Day by day it is increasing massively. There are enough opportunities for research to retrieve knowledge from the data available in this information. Data mining and app

Complex event recognition through wearable sensors

Complex events are instrumental in understanding advanced behaviours and properties of a system. They can represent more meaningful events as compared to simple events. In this thesis we propose to use wearable sensor signals to detect complex events. These signals are pertaining to the user's state and therefore allow us to understand advanced characteristics about her. We propose a hierarchical approach to detect simple events from the wearable sensors data and then build complex events on top of them. In order to address privacy concerns that rise from the use of sensitive signals, we propose to perform all the computation on device. While this ensures the privacy of the data, it poses the problem of having limited computational resources. This problem is tackled by introducing energy efficient approaches based on incremental algorithms. A second challenge is the multiple levels of noise in the process. A first level of noise concerns the raw signals that are inherently imprecise (e.g. inaccuracy in GPS readings). A second level of noise, that we call semantic noise, is present among the simple events detected. Some of these simple events can disturb the detection of complex events effectively acting as noise. We apply the hierarchical approach in two different contexts defining the two different parts of our thesis. In the first part, we present a mobile system that builds a representation of the user's life. This system is based on the episodic memory model, which is responsible for the storage and recollection of past experiences. Following the hierarchical approach, the system processes raw signals to detect simple events such as places where the user stayed a certain amount of time to perform an activity, therefore building sequences of detected activities. These activities are in turn processed to detect complex events that we call routines and that represent recurrent patterns in the life of the user. In the second part of this thesis, we focus on the detection of glycemic events for diabetes type-1 patients in a non-invasive manner. Diabetics are not able to properly regulate their glucose, leading to periods of high and low blood sugar. We leverage signals (Electrocardiogram (ECG), accelerometer, breathing rate) from a sport belt to infer such glycemic events. We propose a physiological model based on the variations of the ECG when the patient has low blood sugar, and an energy-based model that computes the current glucose level of the user based on her glucose intake, insulin intake and glucose consumption via physical activity. For both contexts, we evaluate our systems in term of accuracy by assessing wether the detected routines are meaningful, and wether the glycemic events are correctly detected, and in term of mobile performance, which confirms the fitness of our approaches for mobile computation