Energy efficient privacy preserved data gathering in wireless sensor networks having multiple sinks

Wireless sensor networks (WSNs) generally have a many-to-one structure so that event information flows from sensors to a unique sink. In recent WSN applications, many-tomany structures are evolved due to need for conveying collected event information to multiple sinks at the same time. This study proposes an anonymity method bases on k-anonymity for preventing record disclosure of collected event information in WSNs. Proposed method takes the anonymity requirements of multiple sinks into consideration by providing different levels of privacy for each destination sink. Attributes, which may identify of an event owner, are generalized or encrypted in order to meet the different anonymity requirements of sinks. Privacy guaranteed event information can be multicasted to all sinks instead of sending to each sink one by one. Since minimization of energy consumption is an important design criteria for WSNs, our method enables us to multicast the same event information to multiple sinks and reduce energy consumption

Privacy preserving data collection framework for user centric network applications

Advances in mobile and ubiquitous computing increased the number of user centric applications that comes into all aspects of our lives. This situation has started to threaten our privacy and created a huge demand for development of privacy-aware applications. Comprehensive privacy protection mechanisms have to take all phases of data processing into considerations including data collection from users, storage of data in central servers, and sharing them with third parties. However, privacy studies in the literature generally bring solutions for sharing of collected information with third parties. In this thesis, a privacy preserving data collection framework is proposed for user centric network applications. Framework provides privacy of data en route to data collector(s). We propose a generic bottom-up clustering method that utilizes k-anonymity or l-diversity concepts during anonymization. Entropy based metrics for information loss and anonymity level are defined and used in performance evaluations. Framework is adapted for networks having different data collector parties with different privacy levels. Our framework is applied for two types of data collection applications: (i) privacy preserving data collection in wireless sensor networks, (ii) preservation of organiza- tional privacy during collection of intrusion detection logs from different organiza- tions. Traditional data utility vs. privacy trade-off has one more dimension in wireless sensor networks. This dimension is minimization of bandwidth or energy consump- tion due to the limitations of tiny sensor nodes. Our analyses show that the proposed framework presents a suitable trade-off mechanism among energy consumption minimization, data utility and privacy preservation in wireless sensor network applications with one or multiple sinks. It is also demonstrated that our framework brings effective solution for preserving organizational privacy during sharing of intrusion detection logs among organizations and central security monitoring entity

Inferring phylogenetical tree by using hierarchical self organizing maps

Ankara : The Department of Computer Engineering and Institute of Engineering and Sciences of Bilkent Univ., 2002.Thesis (Master's) -- Bilkent University, 2002.Includes bibliographical references leaves 57-58.In biology, inferring phylogenetical tree is an attempt to describe the evolutionary history of today’s species with the aim of finding their common ancestors. Specifically in molecular biology, it is used in understanding the evolution relationships between proteins or DNA sequences. Inferring phylogenetical tree can be a very complicated task since even for the input data having thirty sequences, the best tree must be chosen among 1036 possible trees. In order to find the best one in a reasonable time, various hierarchical clustering techniques exist in the literature. On the other side, it is known that Self Organizing Maps (SOM) are very successful in mapping higher dimensional inputs to two dimensional output spaces (maps) without having any priori information about input patterns. In this study, SOM are used iteratively for tree inference. Two different algorithms are proposed. First one is hierarchical top-down SOM method which constructs the tree from the root to the leaves. Second one uses a bottom-up approach that infers the tree from the leaves to the root. The efficiency of Hierarchical SOM is tested in terms of tree topology. Hierarchical SOM gives better results than the most popular phylogeny methods, UPGMA and Neighbor-joining. Also this study covers possible solutions for branch length estimation problem.Bahşi, HayretdinM.S

Classification of confidential documents by using adaptive neurofuzzy inference systems

AbstractDetecting the security level of a confidential document is a vital task for organizations to protect the confidential information encapsulated in. Diverse classification rules and techniques are being applied by human experts. Increasing number of confidential information in organizations are making difficult to classify all the documents carefully with human effort. A hybrid approach involving support vector classifier and adaptive neuro-fuzzy classifier is proposed in this study. Also states preprocessing tasks required for document classification with natural language processing. To represent term-document relations a recommended metric TF-IDF was chosen to construct a weight matrix. Agglutinative nature of Turkish documents is handled by Turkish stemming algorithms. At the end of the article some experimental results and success metrics are projected with accuracy rates

k-anonymity based framework for privacy preserving data collection in wireless sensor networks

In this paper; k-anonymity notion is adopted to be used in wireless sensor networks (WSN) as a security framework with two levels of privacy. A base level of privacy is provided for the data shared with semi-trusted sink and a deeper level of privacy is provided against eavesdroppers. In the proposed method, some portions of data are encrypted and the rest is generalized. Generalization shortens the size of the data transmitted in the network causing energy saving at the cost of information loss. On the other hand, encryption provides anonymization with no information loss and without saving energy. Thus, there is a tradeoff between information loss and energy saving. In our system, this tradeoff is intelligently managed by a system parameter, which adjusts the amount of data portions to be encrypted. We use a method based on bottom up clustering that chooses the data portions to be encrypted among the ones that cause maximum information loss when generalized. In this way; a high degree of energy saving is realized within the given limits of information loss. Our analysis shows that the proposed method achieves the desired privacy levels with low information loss and with considerable energy saving