Browsing by Author "Barker, Ken"

Now showing 1 - 20 of 21
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    The 2DR-tree: A 2-dimensional spatial access method
    (2004-05-04) Osborn, Wendy; Barker, Ken
    This paper presents the 2DR-tree, a novel approach for accessing spatial data. The 2DR-tree uses nodes that are the same dimensionality as the data space. Therefore, all relationships between objects are preserved and different searching strategies such as binary and greedy are supported. The insertion and deletion strategies both use a binary partition of a node to insert an object or update a non-leaf minimum bounding rectangle. A validity test ensures that each node involved in an insertion or deletion preserves the spatial relationships among its objects. A performance evaluation shows the advantages of the 2DR-tree and identifies issues for future consideration.
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    AUTOMATIC INTEGRATION OF RELATIONAL DATABASE SCHEMAS
    (2000-10-16) Lawrence, Ramon; Barker, Ken
    This paper focuses on capturing the semantics of data stored in databases with the goal of integrating data sources within a company, across a network, and even on the World-Wide Web. Our approach to capturing data semantics revolves around the definition of a standardized dictionary which provides terms for referencing and categorizing data. These standardized terms are then stored in semantic specifications called X-Specs which store metadata and semantic descriptions of the data. Using these semantic specifications, it becomes possible to integrate diverse data sources even though they were not originally designed to work together. The centralized version of the architecture is presented which allows for the independent integration of data source information (represented using X-Specs) into a unified view of the data. The architecture preserves full autonomy of the underlying databases which are transparently accessed by the user from a central portal. Distributing the architecture would by-pass the central portal and allow integration of web data sources to be performed by a user's browser. Such a system which achieves automatic integration of data sources would have a major impact on how the Web is used and delivered. Unlike wrapper or mediator systems which achieve data source integration by manually defining an integrated view, our architecture automatically constructs an integrated view from information independently provided by the data sources. Thus, the contribution is an algorithm for schema integration not just a methodology for accessing data sources whose knowledge has been precombined into mediated views. The integrated view is a hierarchy of concepts that is queried by semantic name. Thus, the system provides both logical and physical access transparency by mapping user queries on high-level concepts to physical schema elements in the underlying data sources. Notes: Joint released technical report. Released as TR-00-15 for the University of Manitoba, and 2000-662-14 for the University of Calgary.
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    The Design of a Biologically Inspired Peer-to-Peer Distributed File System
    (2004-02-12) Camorlinga, Sergio; Barker, Ken
    This paper introduces the design of a
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    Distributed Deep Learning Methods for Medical Imaging Analysis
    (2024-10-29) Souza De Andrade, Raissa Cristina; Forkert, Nils Daniel; Wilms, Matthias; Pike, G. Bruce; Barker, Ken
    Recent advancements in deep learning have equipped healthcare professionals with valuable tools to support clinical decision-making and reduce workloads. However, many medical centers lack sufficient datasets to train deep learning models, especially for rare diseases or centers in remote or underserved areas. Although collecting and curating datasets from multiple centers into a centralized repository is commonly employed to solve this problem, this approach is often infeasible due to its costs and privacy regulations that prohibit data sharing. Consequently, many centers and populations will not benefit from cutting-edge artificial intelligence. The distributed deep learning framework proposed in this work addresses these challenges by training accurate models while patient data remains securely stored within its center. Thus, privacy concerns are addressed while collaborative multi-center training is facilitated. A key innovation of this work is the development and evaluation of the travelling model, a method well-suited for scenarios where individual centers have very limited data availability. The travelling model is evaluated across various scenarios, including extreme cases where centers contribute only a single medical image, and is applied to critical medical imaging tasks such as brain age prediction, disease classification, and tumour segmentation. In general, the travelling model effectively increases the overall dataset quantity and diversity without compromising patient data privacy. However, solutions for the inherent acquisition shift biases caused by variations in equipment and protocols across centers and decentralized data quality control are needed to leverage its full potential. Therefore, this work also developed and integrated two novel methods into the travelling model approach, a data harmonization for reducing acquisition shift biases and automated decentralized data quality control. The results of this work demonstrate that the travelling model framework achieved performances comparable to models trained on a centralized repository across all evaluated tasks. Moreover, it performed better than the commonly used federated learning in cases where centers contributed fewer than five datasets. Additionally, the proposed data harmonization method reduced scanner variability by 23%, improving disease classification accuracy by 4%. Finally, the automated decentralized quality control method effectively identified incorrect and low-quality datasets, enabling more robust and reliable model performance.
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    Efficient Broadcast Schedulers of Hierarchical Data Dissemination Systems
    (2005-07-18) Omotayo, Adesola; Hammad, Moustafa A.; Barker, Ken
    With the increasing popularity of portable wireless devices and the need to access data anytime and anywhere, mechanisms to efficiently and effectively transmit information to wireless clients are of significant interest. Several research studies address broadcast scheduling algorithms for centralized systems. However, broadcast scheduling in hierarchical data dissemination systems are largely ignored. In these systems a primary server accepts updates that are broadcasted to secondary servers and then to wireless clients. This paper focuses on broadcast scheduling at the primary server side. First, we show that a straightforward broadcast scheduler that ignores clients' access patterns can provide participating clients with outdated information more than 80% of the time. Then, we propose three broadcast scheduling algorithms. The proposed algorithms primarily differ in how data broadcasts are guided at the primary and secondary servers. We present guidance mechanisms that are based on real and predicted clients' access patterns. We experimentally evaluate the proposed scheduling algorithms using simulation while running an extensive set of experiments. The performance study illustrates that the third proposed algorithm, which depends on predictive scheduling at both the primary and the secondary servers, provides the best performance in terms of the response time of the clients' requests and the reception of outdated information.
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    A Framework for Expressing and Enforcing Purpose-Based Privacy Policies
    (2013-01-28) Jafari, Mohammad; Fong, Philip; Safavi-Naini, Reihaneh; Barker, Ken
    Purpose is a key concept in privacy policies and has been mentioned in major privacy laws and regulations. Although some models have been proposed for enforcing purpose-based policies, little has been done in de ning formal semantics for purpose and therefore an e ective enforcement mechanism for policies has remained a challenge. In this paper, we develop a framework for formalizing and enforcing purpose-based privacy policies. Purpose is formally de ned as the dynamic situation of an action within the network of inter-related actions in the system. Accordingly, we propose a modal-logic language for formally expressing constraints about purposes of actions which can be used to model purpose-based policies. The semantics of this language are de ned over an abstract model of activities in the system which is directly derivable from business processes. Based on this formal framework, we discuss some properties of purpose and show how some well-known, as well as new forms of purpose constraints can be formalized using the proposed language. We also show how purpose-based constraints can be tied to other access control policies in the system. Finally, we present a model-checking algorithm for verifying whether a given state of the system complies with a given set of policies, followed by a discussion of how this can be used in an actual implementation of a purpose reference monitor.
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    Multiagent Systems Storage Resource Allocation in a Peer-to-Peer Distributed File System
    (2003-01-28) Camorlinga, Sergio; Barker, Ken
    The objective of this research project is to understand and develop Multiagent Systems (MAS) storage resource allocation algorithms and methods where a peer-to-peer (P2P) system of computer resources is seen as a community of peers with different capabilities that collaborate to enhance the global performance in terms of storage resource balancing. The research focuses on P2P Distributed File Systems (DFS) storage resource allocation algorithms. Complex adaptive systems are evaluated and used to produce emergent global behaviours that can solve the storage resource allocation problem in a distributed system of peers. Squirrel behaviours provide a metaphor to develop algorithms and methods to allocate resources. A complete simulation software tool was developed to implement different behaviours that are analyzed on different scenarios and experiments. Experimental results support the initial hypothesis that hoarding mechaninsms found on squirrels behaviours allocate efficiently resources on a distributed system of consumers and providers of storage resources.
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    MULTIDATABASE QUERYING BY CONTEXT
    (2000-10-16) Lawrence, Ramon; Barker, Ken
    The overwhelming acceptance of the SQL standard \cite{Date94} has curtailed continuing research work in relational database query languages and processing. Since all commercial relational database systems conform with the SQL standard, there is little motivation for developing new query languages. Despite its benefits and wide-spread acceptance, SQL is not a perfect query language. Complex database schema challenge even experienced database users during query formulation. As increasing numbers of less sophisticated users access numerous data sources within an organization or across the Internet, their ability to accurately construct queries with the appropriate structure and semantics diminishes. SQL can be hard to use as it provides only physical access transparency not logical transparency. That is, a user is responsible for mapping the semantics of their query to the semantics and structure of the database. Although graphical tools for query construction and high-level programming languages mask some of the complexity, the notion of querying by structure is intrinsic to most forms of data access. In this work, we overview a new query language developed in conjunction with our integration architecture for automatically integrating relational schema. Although the major focus of this work is on database interoperability, the contribution of this paper is a language for specifying queries on the integrated view produced. The complexities of querying across database systems and resolving conflicts are too numerous to be fully described here, so this paper will discuss querying the integrated view of a single database. The integration architecture integrates database schema information into a context view (CV). The context view is a high-level view of database semantics which allows logically and physically transparent access to the underlying data source(s). Since this context view is an entirely new way of organizing and categorizing database information, a new query language is developed. However, we demonstrate that the context view has similar properties as the Universal Relational Model and thus can benefit from its associated algorithms and ideas. By allowing the user to query by context and semantic connotation, a whole new level of query complexity arises. Mapping of queries from semantic concepts to physical tables, fields, and relationships must be automatically performed. We will demonstrate that specific relational calculus expressions or SQL queries can be generated from abstract concepts which are rigorous enough for use in industrial applications and systems. Specifically, SQL generation and join discovery are overviewed. Thus, the query language can be mapped to SQL allowing backwards compatibility with existing systems. Notes: Joint released technical report. Released as TR-00-16 for the University of Manitoba, and 2000-663-15 for the University of Calgary.
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    A Negotiation Game: Establishing Stable Privacy Policies for Aggregate Reasoning
    (2012-10-31T14:47:10Z) Barker, Ken; Denzinger, Joerg; Karimi Adl, Rosa
    The process of personal information collection and exchange is associated with ever-growing privacy concerns. To resolve the issue, data provider's consent on the usage of private information is sought through privacy policy speci cations. The parameters of such privacy policies in uence the quantity and quality of gathered information. Choosing the right privacy policy parameters can potentially increase the revenues to a data collector and the rms (third-parties) interested in accessing the database for data analysis purposes. In this work we use an extensive form game model to examine the decisions made by a data collector and a third-party to maximize their bene ts from collecting and accessing data. We have found the game's subgame perfect equilibria for various problem settings and provide the details of game analysis for a simpli ed scenario and two case studies. The equilibrium solutions demonstrate steady states of the game where collecting personal information at a speci c privacy level is advantageous to the data collector and the third-party. Consequently the results de ne a realistic boundary on collecting personal information.
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    Personalized Privacy Preservation in IoT
    (2022-03) Onu, Emmanuel; Barker, Ken; Patrick Keenan, Thomas; Henry, Ryan; Hengartner, Urs; Safavi-Naini, Rei
    The widespread use and deployment of the Internet of Things (IoT) devices have been instrumental in automating many of our everyday tasks. Its ability to seamlessly integrate and improve the activities in our daily lives has created a wide application for it in several domains, such as smart buildings and cities. However, despite the numerous benefits associated with the integration of the IoT, there are some privacy challenges. These privacy challenges result from the ability of IoT devices to pervasively collect data about their surroundings, which could reveal sensitive information. Though the data may be collected for genuine purposes such as service personalization, previous research has identified two fundamental causes of privacy concern with data collection: 1) the lack of awareness of the presences and practices of data collecting IoT devices, and 2) the lack of control over data collected by these devices. Current efforts to address the issue of privacy awareness raise a new problem of how to deal with the cognitive burden associated with making several privacy decisions across different contexts. In addition, very little work has developed approaches for giving users control over their privacy in a smart environment. To address the privacy challenges with the IoT, it is vital to create a privacy-sensitive smart environment. A core tool required for such an environment is an intelligent personalized privacy assistant that will mediate the interactions between users and IoT devices around them. Some of the essential requirements for this privacy assistant include notification about data collecting IoT devices, user preference capturing, and privacy recommendations. In this research, we focus on some of the vital requirements for this privacy-preserving smart environment, which include IoT privacy policy modeling, user preference evaluation, user privacy preference prediction, and privacy contract negotiation. Privacy policy modeling is essential for creating privacy awareness and capturing users' preferences. We present important privacy dimensions that should be contained within an IoT privacy policy. Additionally, an understanding of people's privacy preferences is key to giving them control over their privacy and creating a more privacy-sensitive environment. We propose a workflow for analyzing three key preferences of people in an IoT environment: Notification, Control, and Permission. Furthermore, we offer a novel approach for predicting people's privacy preferences using a hybrid of Knowledge-based and Collaborative Filtering (CF), an approach commonly employed in recommender systems. Our approach is based on the premise that people share similar privacy preferences. Therefore, we predict the privacy decisions of a person by considering the privacy decisions made by people who are like them and have made privacy decisions in a similar context. The semantic similarity between two IoT contexts is established through the help of a taxonomy defined over each variable that composes the context. We then evaluate the efficiency of our approach using a dataset that contains the privacy preferences of 172 participants obtained in a simulated campus-wide IoT environment. Finally, we present a privacy contract negotiation protocol for the IoT based on the infrastructures in our privacy-preserving smart environment framework.
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    Privacy Consensus in Anonymization Systems Via Game Theory
    (2012-03-01T18:26:47Z) Karimi Adl, Rosa; Askari, Mina; Barker, Ken; Safavi-Naini, Reihaneh
    Privacy protection appears as a fundamental concern when personal data is collected, stored, and published. Several anonymization methods have been proposed to protect individuals' privacy before data publishing. Each anonymization method has at least one parameter to adjust the level of privacy protection. Choosing a desirable level of privacy protection is a crucial decision because it affects the volume and usability of collected data differently. In this paper, we demonstrate how to use game theory to model different and conflicting needs of parties involved in making such decision. We describe a general approach to solve such games and elaborate the procedure using k-anonymity as a sample anonymization method. Our model provides a generic framework to find stable values for privacy parameters within each anonymization method, to recognize the characteristics of each anonymization method, and to compare different anonymization methods to distinguish the settings that make one method more appealing than the others.
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    Privacy Preserving Search Techniques over Encrypted Outsourced Data
    (2020-07-24) Salmani, Khosro; Barker, Ken; Jacobson, Michael; Reardon, Joel
    During the last decade, various type of cloud services have encouraged individuals and enterprises to store personal data in the cloud. Despite its flexibility, cost efficiency, and convenient service, protecting security and privacy of the outsourced data has always been a primary challenge. Although data encryption retains the outsourced data's security and privacy to some extent, it does not permit traditional plaintext keyword search mechanisms, and it comes at the cost of efficiency. Hence, proposing an efficient encrypted cloud data search service would be an important step forward. To address this challenge scholars introduced Searchable Symmetric Encryption (SSE) in which a client is able to perform searches over encrypted documents. However, these schemes suffer from private information leakage such as access pattern, search pattern, and co-occurrence leakage. Several recent papers show that how this critical information can be exploited to collapse the whole security system and an adaptive attacker can reveal plaintext data. In this thesis, we address the challenging problems of access pattern, search pattern, and co-occurrence private information leakage. We design and construct three schemes to tackle the above challenges. We formally prove that all of our schemes are secure and achieve a higher level of privacy by preventing and obfuscating the private information leakages. Moreover, our performance analyses demonstrate the practicality and efficiency of our approaches.
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    Redeem with Privacy (RwP): Privacy protection framework for Geo-social commerce
    (2022-09) Moniruzzaman, Md.; Barker, Ken; Safavi-Naeini, Rei; Willett, Wesley
    Geo-social networks (GSN) are online social networks where people interact based on their location and relationship. These applications have gained popularity due to their innovative features. However, there are numerous privacy risks of using GSNs. Users may expose their mobility history to unknown third parties since many of these applications rely on collecting and sharing users' information. Business organizations encourage people to do a check-in to their store on GSNs by offering promotions and deals. Check-in is a virtual form of visiting a location. When a user performs a check-in to a business organization, the record is shared with the merchant. GSNs lack transparency in explaining how the third parties handle users' information. In practice, a dishonest merchant may use check-in histories to track the user's location. It may cause privacy breaches like robbery, disclosure of meetings, stalking, etc. In my Ph.D. thesis, I investigate privacy issues arising from deal redemption in GSNs. I perform an exploratory study on several GSN datasets to understand when people visit different types of locations. The study shows that there is a high degree of regularity in the user's check-in behavior. Since a typical deal requires multiple check-ins from the user within a short period, the user may become vulnerable to location tracking by redeeming deals. One potential solution is to minimize the volume of check-in information released when the user redeems deals. In this thesis, I propose a policy to identify redundant information that is not essential for a merchant to know and suppress them. I also explore the possibility that a merchant may apply inference attacks to recover the deleted information. Several inference methodologies have been investigated in my thesis, showing that a merchant can recover the data with high accuracy. I study an adversarial technique to improve a user's privacy by increasing the merchant's inference error. A recommendation algorithm is proposed to rank check-ins that a user can follow to redeem deals. Ranking applies various factors that people consider when choosing a check-in date, such as daily routine, the promotional value, and privacy. Results show how different user preferences map to various levels of inference accuracy. It would provide helpful feedback to users on how to change their preferences to enhance their privacy.
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    Searching the 2DR-tree
    (2004-05-11) Osborn, Wendy; Barker, Ken
    The 2DR-tree is a novel approach for access spatial data, which uses nodes that are the same dimensionality as the data space. Therefore, all relationships between objects are preserved and different binary search strategies are supported. This paper presents the 2DR-tree binary search strategy. Validity rules ensure that each node preserves all spatial relationships. A performance evaluation shows the advantages of the 2DR-tree binary search strategy.
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    Stable Privacy Parameter Settings Using Game Theory
    (2013-04-16) Karimi Adl, Rosa; Barker, Ken
    Privacy protection appears as a fundamental concern when personal data is collected, stored, and published. Several privacy protection methods have been proposed to address privacy issues in private datasets. Each method has at least one parameter to adjust the guaranteed level of privacy protection. As the privacy protection level increases, the dataset loses more information utility due to further application of data manipulation methods and/or access restriction rules. Consequently, balancing the trade ff between privacy and utility is a crucial step and so far no systematic mechanism exists to provide directions on how to establish values for privacy parameters such that a balanced privacy/utility tradeff is induced. A balanced privacy/utility tradeoff can be described as a level on which the stakeholders of data reach a consensus (in the sense that no single party would be wiling to act diff erently to change the agreed upon level). Game theory provides a natural solution to finding such balanced tradeoff s. In this thesis, we capture the essence of establishing balancing values for privacy parameters as an extensive-form game with incomplete and imperfect information. A high-level step-by-step guideline is provided on how to solve the generic game. We instantiate the generic game model for three different privacy protection methods and analytically solve each game. The games' solutions are further simulated for sample problem settings to study the effects of various problem parameters on the balancing values of privacy parameters. The game model and its solution contribute to the fulfillment of our objective of establishing balancing values for privacy parameters (of a chosen privacy protection method). In addition to our main objective, the proposed game model can be consulted to choose the most pro fitable privacy protection method based on the problem requirements. Benchmarking frameworks can also benefi t from our game solutions by using the balancing privacy parameter values as the reference points for the comparisons between different privacy protection methods. We believe that a first step towards improving the data collection and privacy protection procedures is to understand how much privacy is currently sacrificed to achieve information utility (at the steady states). The game-based solution provided in this thesis promotes a deeper understanding of how privacy and utility reach a balanced tradeoff within the current privacy protection methods.
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    The Virtual Faraday Cage
    (2013-08-09) King, James; Barker, Ken; Kawash, Jalal
    This thesis' primary contribution is that of a new architecture for web application platforms and their extensions, entitled "The Virtual Faraday Cage". This new architecture addresses some of the privacy and security related problems associated with third-party extensions running within web application platforms. A proof-of-concept showing how the Virtual Faraday Cage could be implemented is described. This new architecture aims to help solve some of the key security and privacy concerns for end-users in web applications by creating a mechanism by which a third-party could create an extension that works with end-user data, but which could never leak such information back to the third-party. To facilitate this, the thesis also incorporates a basic privacy-aware access control mechanism. This architecture could be used for centralized web application platforms (such as Facebook) as well as decentralized platforms. Ideally, the Virtual Faraday Cage should be incorporated into the development of new web application platforms, but could also be implemented via wrappers around existing application platform Application Programming Interfaces with minimal changes to existing platform code or workflows.
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    UNITY - A DATABASE INTEGRATION TOOL
    (2000-10-16) Lawrence, Ramon; Barker, Ken
    The World-Wide Web (WWW) provides users with the ability to access a vast number of data sources distributed across the planet. Internet protocols such as TCP/IP and HTTP have provided the mechanisms for exchanging the data. However, a fundamental problem with distributed data access is the determination of semantically equivalent data. Ideally, users should be able to extract data from multiple Internet sites and have it automatically combined and presented to them in a usable form. No system has been able to accomplish these goals due to limitations in expressing and capturing data semantics. This paper details the construction, function, and deployment of Unity, a database integration software package which allows database semantics to be captured so that they may be automatically integrated. Unity is the tool that we use to implement our integration architecture detailed in previous work. Our integration architecture focuses on capturing the semantics of data stored in databases with the goal of integrating data sources within a company, across a network, and even on the World-Wide Web. Our approach to capturing data semantics revolves around the definition of a standardized dictionary which provides terms for referencing and categorizing data. These standardized terms are then stored in semantic specifications called X-Specs which store metadata and semantic descriptions of the data. Using these semantic specifications, it becomes possible to integrate diverse data sources even though they were not originally designed to work together. The centralized version of the architecture is presented which allows for the independent integration of data source information (represented using X-Specs) into a unified view of the data. The architecture preserves full autonomy of the underlying databases which are transparently accessed by the user from a central portal. Distributing the architecture would by-pass the central portal and allow integration of web data sources to be performed by a user's browser. Such a system which achieves automatic integration of data sources would have a major impact on how the Web is used and delivered. Unity is the bridge between concept and implementation. Unity is a complete software package which allows for the construction and modification of standardized dictionaries, parsing of database schema and metadata to construct X-Specs, and contains an implementation of the integration algorithm to combine X-Specs into an integrated view. Further, Unity provides a mechanism for building queries on the integrated view and algorithms for mapping semantic queries on the integrated view to structural (SQL) queries on the underlying data sources. Notes: Join released technical report. Released as TR-00-17 for the University of Manitoba, and 2000-664-16 for the University of Calgary.
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    A Workflow Reference Monitor for Enforcing Purpose-Based Policies
    (2013-09-25) Jafari, Mohammad; Denzinger, Joerg; Safavi-Naini, Reihaneh; Barker, Ken
    Purpose is a key concept in privacy policies. Based on the purpose framework developed in our earlier work [11] we present an access control model for a work ow-based information system in which a work ows reference monitor ( WfRM ) enforces purpose-based policies. We use a generic access control policy language and show how it can be connected to the purpose modal logic language ( PML ) to link purpose constraints to access control rules and how such policies can be enforced. We also present a simple implementation of such a reference monitor based on extending eXtensible Access Control Markup Language( XACML ), a commonly used access control open standard.
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    WORLD WIDE WEB DATABASE INTEGRATION VIA MOBILE AGENTS
    (2002-12-06) Trinh, Quang; Barker, Ken; Alhajj, Reda
    This paper presents an architecture that makes use of dynamic deployment of mobile agents to support both integration of heterogeneous/homogeneous databases on the World Wide Web (or web) and data exchange between the architecture and existing applications. The system uses an integrated and global schema to utilize agents in remote locations and HyperText Markup Language (HTML - usable even with minimal bandwidth) as the client interface and to control agents behind the scene. Using HTML reduces overhead and therefore gives the architecture performance advantages over other existing mobile agent systems. Furthermore, the system also supports the addition and removal of component databases dynamically. The exchange language used by the system is XML (eXtensible Markup Language), a standard and extensible format with supports in almost all programming languages, which can be read and understood by almost any application. With rich document structures like XML, not only can data transported by the Agents be used over the web, but it can also be used for Business To Business (B2B) data exchange between existing and/or future applications. The proposed system is shown to be better than existing techniques when higher volumes of data are to be exchanged such as is typical B2B applications. The improvements are consistently significant and are magnified substantially as the amount of data exchanged increases.
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    XML Schema Reduction Algorithm
    (2004-06-16) Duta, Angela; Barker, Ken; Alhajj, Reda
    XML file comparison and clustering are two challenging tasks still accomplished predominantly manually. XML schema contains information about data structure, types, and labels found in an XML file. By reducing the XML schema tree to its significant nodes the task of finding equivalent schemas, and implicit XML files that refer to the same entities, is simplified.