Ahmad Kamran Malik

A Hybrid Sharing Control Model for Context Sharing and Privacy in Collaborative Systems

Complex Web-based information systems involving multiple entities and their dynamic mobile-based collaborations require efficient techniques for context information sharing. Sharing control is a requirement for preserving the privacy of personal context and shared context. Our sharing control mechanism is hybrid, based on sharing control rules defined by enterprise as well as by individuals users. Our complex scenario involves multiple entities which require prioritization and conflict handling mechanism for entities and their policy rules. This paper presents a sharing control model, Web services-based architecture and its implementation with a running example. The system is evaluated by comparing our hybrid sharing control policy with enterprise-defined role -based policy and shows effectiveness of our hybrid policy in collaborative information sharing environments.

Sharing and Privacy-Aware RBAC in Online Social Networks

Online social networks have gained enormous popularity in the last decade. Users share their private information online with other users, which is becoming a serious privacy issue at the social and technical level. In a society, everyone is a member of many collaborative groups (social circles), for example, colleagues, class fellows. There are many collaborative relationships among group members. Current social networks mostly use friend as a relationship, which in our social life, is not practical. In a society, we have different levels of collaborative relationships with others and even some relationship with non-friends. We use collaborative groups and relationships that facilitate users in controlling the privacy and sharing level of their information in social networks. We present a sharing and privacy-aware SP-RBAC model by extending the well-known RBAC model. Our model increases the collaborative community of a user, and hence increases sharing of information, minimizing the privacy threats using simple user management.

Enhanced sharing and privacy in distributed information sharing environments

With the advancement in distributed computing and collaborative software technologies, information sharing and privacy related issues are gaining interest of researchers related to digital information creation, management, and distribution. Collaborative information sharing environment requires enhanced information sharing among users while privacy laws demand for the protection of users information from unauthorized access and usage. Keeping this trade-off in view, there is a need for a flexible and enhanced information sharing model that preserves the privacy of users information. We extend the Role-Based Access Control (RBAC) model to incorporate sharing and privacy related requirements and present a Dynamic Sharing and Privacy-aware Role-Based Access Control (DySP-RBAC) model. It is a family of models including core, hierarchical, and constrained RBAC models. The RBAC model is extended using team and task data elements as well as new data elements related to sharing and privacy of information. Sharing and privacy-based permission assignments and their conflict-handling strategies are described for a distributed and dynamic information sharing scenario.

DySCon: Dynamic Sharing Control for Distributed Team Collaboration in Networked Enterprises

Networked enterprises create virtual teams of distributed experts belonging to different enterprises where one user can be part of multiple teams; How to effectively control the sharing of personal and shared context information among members of multiple overlapping teams without compromising their privacy is a challenging research question. This paper describes sharing control in Peer to Peer and Web service based collaborative systems. In contrast to other sharing systems which mostly use static policy and context of requester and owner, we propose DySCon, a context based Dynamic Sharing Control mechanism which allows system defined as well as owner defined runtime policy adaptation for different levels using various contexts. We evaluate our Dynamic Sharing Control architecture by implementing a prototype Dynamic Sharing Control Messenger to enhance privacy of the owner.

An intelligent information sharing control system for dynamic collaborations

Information sharing is a basic requirement in Collaborative Working Environment (CWE), yet privacy of the owner of shared information needs special attention. Personal information and shared information coexist in enterprise-based users, teams, and their activities. Distributed and dynamic collaborations in CWE are a challenge for privacy preservation due to lack of trust among users and thus require an intelligent sharing control policy. Our system intelligently monitors and analysis status of collaborating users, plans sharing control activities according to changing collaborative relationships, user interactions, and context conditions which help in dynamic adaptation of sharing control policy. We present an intelligent sharing control architecture, its implementation, and discussion using Web services and an intelligent context sharing messenger application.

Performance prediction and adaptation for database management system workload using Case-Based Reasoning approach

Abstract Workload management in a Database Management System (DBMS) has become difficult and challenging because of workload complexity and heterogeneity. During and after execution of the workload, it is hard to control and handle the workload. Before executing the workload, predicting its performance can help us in workload management. By knowing the type of workload in advance, we can predict its performance in an adaptive way that will enable us to monitor and control the workload, which ultimately leads to performance tuning of the DBMS. This study proposes a predictive and adaptive framework named as the Autonomic Workload Performance Prediction (AWPP) framework. The proposed AWPP framework predicts and adapts the DBMS workload performance on the basis of information available in advance before executing the workload. The Case-Based Reasoning (CBR) approach is used to solve the workload management problem. The proposed CBR approach is compared with other machine learning techniques. To validate the AWPP framework, a number of benchmark workloads of the Decision Support System (DSS) and the Online Transaction Processing (OLTP) are executed on the MySQL DBMS. For preparation of training and testing data, we executed more than 1000 TPC-H and TPC-C like workloads on a standard data set. The results show that our proposed AWPP framework through CBR modeling performs better in predicting and adapting the DBMS workload. DBMSs algorithms can be optimized for this prediction and workload can be controlled and managed in a better way. In the end, the results are validated by performing post-hoc tests.

A Comparison of Collaborative Access Control Models

Collaborative environments need access control to data and resources to increase working cooperation efficiently yet effectively. Several approaches are proposed and multiple access control models are recommended in this domain. In this paper, four Role-Based Access Control (RBAC) based collaborative models are selected for analysis and comparison. The standard RBAC model, Team-based Access Control (TMAC) model, Privacy-aware Role-Based Access Control (P-RBAC) model and Dynamic Sharing and Privacy-aware RBAC (DySP-RBAC) model are used for experiments. A prototype is developed for each of these models and pros and cons of these models are discussed. Performance and sharing parameters are used to compare these collaborative models. The standard RBAC model is found better by having a quick response time for queries as compared to other RBAC models. The DySP-RBAC model outperforms other models by providing enhanced sharing capabilities.

Personal vs. know-how contacts: which matter more in wiki elections?

The use of online social media is also connected with the real world. A very common example of this is the effect of social media coverage on the chances of success of elections. Previous literature has identified that the outcome of elections can often be predicted based on online public discussions. These discussions can be across various online social network with a special focus on the candidates own accounts. Among many other forms of social media, Wikipedia is a very widely-used self-organizing information resource. The management and administration of Wikipedia is performed using special users which are elected by means of online public elections. In other words, the results of these elections pose as an emergent outcome of a large-scale self-organized opinion formation process. However, due to dynamical, and non-linear interactions besides the presence of mutual dependencies between election participants, a statistical analysis of this data can both be cumbersome as well as inefficient in terms of information extraction. We believe that social network analysis is a more appropriate alternative. It allows for the identification of local and global patterns, identification of influential nodes as well as the contacts involved in the influence. In general, this particular analytic technique can help in examining the internal complex network dynamics. In the current paper, we investigates whether personal contacts matter more than know-how contacts in wiki election nominations and voting participation. We employ the use of standard social network analysis tools such as Pajek and Gephi. The presented work demonstrates the significance of personal contacts over know-how contacts of a person in online elections. We have discovered that personal contacts, i.e. immediate neighbors (based on degree centrality) and neighborhood (k-neighbors) of a person have a positive effect on a person’s nomination as an administrator and also contribute to the active participation of voters in voting. Moreover, know-how contacts, analyzed by means of measures such as betweenness and closeness centralities, have a relatively insignificant effect on the selection of a person. However, know-how contacts, measured in terms of betweenness centrality can positively contribute only to the voting process—primarily due to the role played in passing information around the network. These contacts, also measured in terms of influence domain and PageRank, can play a vital role in the selection of an admin. Additionally, such contacts have a positive association with the voting process in terms of reachability and brokerage roles.

Autonomic workload performance tuning in large-scale data repositories

AbstractThe workload in large-scale data repositories involves concurrent users and contains homogenous and heterogeneous data. The large volume of data, dynamic behavior and versatility of large-scale data repositories is not easy to be managed by humans. This requires computational power for managing the load of current servers. Autonomic technology can support predicting the workload type; decision support system or online transaction processing can help servers to autonomously adapt to the workloads. The intelligent system could be designed by knowing the type of workload in advance and predict the performance of workload that could autonomically adapt the changing behavior of workload. Workload management involves effectively monitoring and controlling the workflow of queries in large-scale data repositories. This work presents a taxonomy through systematic analysis of workload management in large-scale data repositories with respect to autonomic computing (AC) including database management systems and data warehouses. The state-of-the-art practices in large-scale data repositories are reviewed with respect to AC for characterization, performance prediction and adaptation of workload. Current issues are highlighted at the end with future directions.

A multi-modal, multi-atlas-based approach for Alzheimer detection via machine learning

The use of biomarkers for early detection of Alzheimers disease (AD) improves the accuracy of imaging‐based prediction of AD and its prodromal stage that is mild cognitive impairment (MCI). Brain parcellation‐based computer‐aided methods for detecting AD and MCI segregate the brain in different anatomical regions and use their features to predict AD and MCI. Brain parcellation generally is carried out based on existing anatomical atlas templates, which vary in the boundaries and number of anatomical regions. This works considers dividing the brain based on different atlases and combining the features extracted from these anatomical parcellations for a more holistic and robust representation. We collected data from the ADNI database and divided brains based on two well‐known atlases: LONI Probabilistic Brain Atlas (LPBA40) and Automated Anatomical Labeling (AAL). We used baselines images of structural magnetic resonance imaging (MRI) and 18F‐fluorodeoxyglucose positron emission tomography (FDG‐PET) to calculate average gray‐matter density and average relative cerebral metabolic rate for glucose in each region. Later, we classified AD, MCI and cognitively normal (CN) subjects using the individual features extracted from each atlas template and the combined features of both atlases. We reduced the dimensionality of individual and combined features using principal component analysis, and used support vector machines for classification. We also ranked features mostly involved in classification to determine the importance of brain regions for accurately classifying the subjects. Results demonstrated that features calculated from multiple atlases lead to improved performance compared to those extracted from one atlas only.