Matching Items (69)

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An Algorithm for Merging Identities

Description

In online social networks the identities of users are concealed, often by design. This anonymity makes it possible for a single person to have multiple accounts and to engage in

In online social networks the identities of users are concealed, often by design. This anonymity makes it possible for a single person to have multiple accounts and to engage in malicious activity such as defrauding a service providers, leveraging social influence, or hiding activities that would otherwise be detected. There are various methods for detecting whether two online users in a network are the same people in reality and the simplest way to utilize this information is to simply merge their identities and treat the two users as a single user. However, this then raises the issue of how we deal with these composite identities. To solve this problem, we introduce a mathematical abstraction for representing users and their identities as partitions on a set. We then define a similarity function, SIM, between two partitions, a set of properties that SIM must have, and a threshold that SIM must exceed for two users to be considered the same person. The main theoretical result of our work is a proof that for any given partition and similarity threshold, there is only a single unique way to merge the identities of similar users such that no two identities are similar. We also present two algorithms, COLLAPSE and SIM_MERGE, that merge the identities of users to find this unique set of identities. We prove that both algorithms execute in polynomial time and we also perform an experiment on dark web social network data from over 6000 users that demonstrates the runtime of SIM_MERGE.

Contributors

Agent

Created

Date Created
  • 2018-05

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Automatic text summarization using importance of sentences for email corpus

Description

With the advent of Internet, the data being added online is increasing at enormous rate. Though search engines are using IR techniques to facilitate the search requests from users, the

With the advent of Internet, the data being added online is increasing at enormous rate. Though search engines are using IR techniques to facilitate the search requests from users, the results are not effective towards the search query of the user. The search engine user has to go through certain webpages before getting at the webpage he/she wanted. This problem of Information Overload can be solved using Automatic Text Summarization. Summarization is a process of obtaining at abridged version of documents so that user can have a quick view to understand what exactly the document is about. Email threads from W3C are used in this system. Apart from common IR features like Term Frequency, Inverse Document Frequency, Term Rank, a variation of page rank based on graph model, which can cluster the words with respective to word ambiguity, is implemented. Term Rank also considers the possibility of co-occurrence of words with the corpus and evaluates the rank of the word accordingly. Sentences of email threads are ranked as per features and summaries are generated. System implemented the concept of pyramid evaluation in content selection. The system can be considered as a framework for Unsupervised Learning in text summarization.

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Agent

Created

Date Created
  • 2015

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Resource allocation in communication and social networks

Description

As networks are playing an increasingly prominent role in different aspects of our lives, there is a growing awareness that improving their performance is of significant importance. In order to

As networks are playing an increasingly prominent role in different aspects of our lives, there is a growing awareness that improving their performance is of significant importance. In order to enhance performance of networks, it is essential that scarce networking resources be allocated smartly to match the continuously changing network environment. This dissertation focuses on two different kinds of networks - communication and social, and studies resource allocation problems in these networks. The study on communication networks is further divided into different networking technologies - wired and wireless, optical and mobile, airborne and terrestrial. Since nodes in an airborne network (AN) are heterogeneous and mobile, the design of a reliable and robust AN is highly complex. The dissertation studies connectivity and fault-tolerance issues in ANs and proposes algorithms to compute the critical transmission range in fault free, faulty and delay tolerant scenarios. Just as in the case of ANs, power optimization and fault tolerance are important issues in wireless sensor networks (WSN). In a WSN, a tree structure is often used to deliver sensor data to a sink node. In a tree, failure of a node may disconnect the tree. The dissertation investigates the problem of enhancing the fault tolerance capability of data gathering trees in WSN. The advent of OFDM technology provides an opportunity for efficient resource utilization in optical networks and also introduces a set of novel problems, such as routing and spectrum allocation (RSA) problem. This dissertation proves that RSA problem is NP-complete even when the network topology is a chain, and proposes approximation algorithms. In the domain of social networks, the focus of this dissertation is study of influence propagation in presence of active adversaries. In a social network multiple vendors may attempt to influence the nodes in a competitive fashion. This dissertation investigates the scenario where the first vendor has already chosen a set of nodes and the second vendor, with the knowledge of the choice of the first, attempts to identify a smallest set of nodes so that after the influence propagation, the second vendor's market share is larger than the first.

Contributors

Agent

Created

Date Created
  • 2014

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Coping with selfish behavior in networks using game theory

Description

While network problems have been addressed using a central administrative domain with a single objective, the devices in most networks are actually not owned by a single entity but by

While network problems have been addressed using a central administrative domain with a single objective, the devices in most networks are actually not owned by a single entity but by many individual entities. These entities make their decisions independently and selfishly, and maybe cooperate with a small group of other entities only when this form of coalition yields a better return. The interaction among multiple independent decision-makers necessitates the use of game theory, including economic notions related to markets and incentives. In this dissertation, we are interested in modeling, analyzing, addressing network problems caused by the selfish behavior of network entities. First, we study how the selfish behavior of network entities affects the system performance while users are competing for limited resource. For this resource allocation domain, we aim to study the selfish routing problem in networks with fair queuing on links, the relay assignment problem in cooperative networks, and the channel allocation problem in wireless networks. Another important aspect of this dissertation is the study of designing efficient mechanisms to incentivize network entities to achieve certain system objective. For this incentive mechanism domain, we aim to motivate wireless devices to serve as relays for cooperative communication, and to recruit smartphones for crowdsourcing. In addition, we apply different game theoretic approaches to problems in security and privacy domain. For this domain, we aim to analyze how a user could defend against a smart jammer, who can quickly learn about the user's transmission power. We also design mechanisms to encourage mobile phone users to participate in location privacy protection, in order to achieve k-anonymity.

Contributors

Agent

Created

Date Created
  • 2013

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Measuring the Impact of Social Network Interactions

Description

Social links form the backbone of human interactions, both in an offline and online world. Such interactions harbor network diffusion or in simpler words, information spreading in a population of

Social links form the backbone of human interactions, both in an offline and online world. Such interactions harbor network diffusion or in simpler words, information spreading in a population of connected individuals. With recent increase in user engagement in social media platforms thus giving rise to networks of large scale, it has become imperative to understand the diffusion mechanisms by considering evolving instances of these network structures. Additionally, I claim that human connections fluctuate over time and attempt to study empirically grounded models of diffusion that embody these variations through evolving network structures. Patterns of interactions that are now stimulated by these fluctuating connections can be harnessed

towards predicting real world events. This dissertation attempts at analyzing

and then modeling such patterns of social network interactions. I propose how such

models could be used in advantage over traditional models of diffusion in various

predictions and simulations of real world events.

The specific three questions rooted in understanding social network interactions that have been addressed in this dissertation are: (1) can interactions captured through evolving diffusion networks indicate and predict the phase changes in a diffusion process? (2) can patterns and models of interactions in hacker forums be used in cyber-attack predictions in the real world? and (3) do varying patterns of social influence impact behavior adoption with different success ratios and could they be used to simulate rumor diffusion?

For the first question, I empirically analyze information cascades of Twitter and Flixster data and conclude that in evolving network structures characterizing diffusion, local network neighborhood surrounding a user is particularly a better indicator of the approaching phases. For the second question, I attempt to build an integrated approach utilizing unconventional signals from the "darkweb" forum discussions for predicting attacks on a target organization. The study finds that filtering out credible users and measuring network features surrounding them can be good indicators of an impending attack. For the third question, I develop an experimental framework in a controlled environment to understand how individuals respond to peer behavior in situations of sequential decision making and develop data-driven agent based models towards simulating rumor diffusion.

Contributors

Agent

Created

Date Created
  • 2020

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Attribute-Based Encryption for Fine-Grained Access Control over Sensitive Data

Description

The traditional access control system suffers from the problem of separation of data ownership and management. It poses data security issues in application scenarios such as cloud computing and blockchain

The traditional access control system suffers from the problem of separation of data ownership and management. It poses data security issues in application scenarios such as cloud computing and blockchain where the data owners either do not trust the data storage provider or even do not know who would have access to their data once they are appended to the chain. In these scenarios, the data owner actually loses control of the data once they are uploaded to the outside storage. Encryption-before-uploading is the way to solve this issue, however traditional encryption schemes such as AES, RSA, ECC, bring about great overheads in key management on the data owner end and could not provide fine-grained access control as well.

Attribute-Based Encryption (ABE) is a cryptographic way to implement attribute-based access control, which is a fine-grained access control model, thus solving all aforementioned issues. With ABE, the data owner would encrypt the data by a self-defined access control policy before uploading the data. The access control policy is an AND-OR boolean formula over attributes. Only users with attributes that satisfy the access control policy could decrypt the ciphertext. However the existing ABE schemes do not provide some important features in practical applications, e.g., user revocation and attribute expiration. Furthermore, most existing work focus on how to use ABE to protect cloud stored data, while not the blockchain applications.

The main objective of this thesis is to provide solutions to add two important features of the ABE schemes, i.e., user revocation and attribute expiration, and also provide a practical trust framework for using ABE to protect blockchain data. To add the feature of user revocation, I propose to add user's hierarchical identity into the private attribute key. In this way, only users whose identity is not revoked and attributes satisfy the access control policy could decrypt the ciphertext. To add the feature of attribute expiration, I propose to add the attribute valid time period into the private attribute key. The data would be encrypted by access control policy where all attributes have a temporal value. In this way, only users whose attributes both satisfy the access policy and at the same time these attributes do not expire,

are allowed to decrypt the ciphertext. To use ABE in the blockchain applications, I propose an ABE-enabled trust framework in a very popular blockchain platform, Hyperledger Fabric. Based on the design, I implement a light-weight attribute certificate authority for attribute distribution and validation; I implement the proposed ABE schemes and provide a toolkit which supports system setup, key generation,

data encryption and data decryption. All these modules were integrated into a demo system for protecting sensitive les in a blockchain application.

Contributors

Agent

Created

Date Created
  • 2020

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SystemC TLM2.0 modeling of network-on-chip architecture

Description

Network-on-Chip (NoC) architectures have emerged as the solution to the on-chip communication challenges of multi-core embedded processor architectures. Design space exploration and performance evaluation of a NoC design requires fast

Network-on-Chip (NoC) architectures have emerged as the solution to the on-chip communication challenges of multi-core embedded processor architectures. Design space exploration and performance evaluation of a NoC design requires fast simulation infrastructure. Simulation of register transfer level model of NoC is too slow for any meaningful design space exploration. One of the solutions to reduce the speed of simulation is to increase the level of abstraction. SystemC TLM2.0 provides the capability to model hardware design at higher levels of abstraction with trade-off of simulation speed and accuracy. In this thesis, SystemC TLM2.0 models of NoC routers are developed at three levels of abstraction namely loosely-timed, approximately-timed, and cycle accurate. Simulation speed and accuracy of these three models are evaluated by a case study of a 4x4 mesh NoC.

Contributors

Agent

Created

Date Created
  • 2012

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A Study of User Behaviors and Activities on Online Mental Health Communities

Description

Social media is a medium that contains rich information which has been shared by many users every second every day. This information can be utilized for various outcomes such as

Social media is a medium that contains rich information which has been shared by many users every second every day. This information can be utilized for various outcomes such as understanding user behaviors, learning the effect of social media on a community, and developing a decision-making system based on the information available. With the growing popularity of social networking sites, people can freely express their opinions and feelings which results in a tremendous amount of user-generated data. The rich amount of social media data has opened the path for researchers to study and understand the users’ behaviors and mental health conditions. Several studies have shown that social media provides a means to capture an individual state of mind. Given the social media data and related work in this field, this work studies the scope of users’ discussion among online mental health communities. In the first part of this dissertation, this work focuses on the role of social media on mental health among sexual abuse community. It employs natural language processing techniques to extract topics of responses, examine how diverse these topics are to answer research questions such as whether responses are limited to emotional support; if not, what other topics are; what the diversity of topics manifests; how online response differs from traditional response found in a physical world. To answer these questions, this work extracts Reddit posts on rape to understand the nature of user responses for this stigmatized topic. In the second part of this dissertation, this work expands to a broader range of online communities. In particular, it investigates the potential roles of social media on mental health among five major communities, i.e., trauma and abuse community, psychosis and anxiety community, compulsive disorders community, coping and therapy community, and mood disorders community. This work studies how people interact with each other in each of these communities and what these online forums provide a resource to users who seek help. To understand users’ behaviors, this work extracts Reddit posts on 52 related subcommunities and analyzes the linguistic behavior of each community. Experiments in this dissertation show that Reddit is a good medium for users with mental health issues to find related helpful resources. Another interesting observation is an interesting topic cluster from users’ posts which shows that discussion and communication among users help individuals to find proper resources for their problem. Moreover, results show that the anonymity of users in Reddit allows them to have discussions about different topics beyond social support such as financial and religious support.

Contributors

Agent

Created

Date Created
  • 2019

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Establishing the software-defined networking based defensive system in clouds

Description

Cloud computing is regarded as one of the most revolutionary technologies in the past decades. It provides scalable, flexible and secure resource provisioning services, which is also the reason

Cloud computing is regarded as one of the most revolutionary technologies in the past decades. It provides scalable, flexible and secure resource provisioning services, which is also the reason why users prefer to migrate their locally processing workloads onto remote clouds. Besides commercial cloud system (i.e., Amazon EC2), ProtoGENI and PlanetLab have further improved the current Internet-based resource provisioning system by allowing end users to construct a virtual networking environment. By archiving the similar goal but with more flexible and efficient performance, I present the design and implementation of MobiCloud that is a geo-distributed mobile cloud computing platform, and G-PLaNE that focuses on how to construct the virtual networking environment upon the self-designed resource provisioning system consisting of multiple geo-distributed clusters. Furthermore, I conduct a comprehensive study to layout existing Mobile Cloud Computing (MCC) service models and corresponding representative related work. A new user-centric mobile cloud computing service model is proposed to advance the existing mobile cloud computing research.

After building the MobiCloud, G-PLaNE and studying the MCC model, I have been using Software Defined Networking (SDN) approaches to enhance the system security in the cloud virtual networking environment. I present an OpenFlow based IPS solution called SDNIPS that includes a new IPS architecture based on Open vSwitch (OVS) in the cloud software-based networking environment. It is enabled with elasticity service provisioning and Network Reconfiguration (NR) features based on POX controller. Finally, SDNIPS demonstrates the feasibility and shows more efficiency than traditional approaches through a thorough evaluation.

At last, I propose an OpenFlow-based defensive module composition framework called CloudArmour that is able to perform query, aggregation, analysis, and control function over distributed OpenFlow-enabled devices. I propose several modules and use the DDoS attack as an example to illustrate how to composite the comprehensive defensive solution based on CloudArmour framework. I introduce total 20 Python-based CloudArmour APIs. Finally, evaluation results prove the feasibility and efficiency of CloudArmour framework.

Contributors

Agent

Created

Date Created
  • 2014

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Web Intelligence for Scaling Discourse of Organizations

Description

Internet and social media devices created a new public space for debate on political

and social topics (Papacharissi 2002; Himelboim 2010). Hotly debated issues

span all spheres of human activity; from liberal

Internet and social media devices created a new public space for debate on political

and social topics (Papacharissi 2002; Himelboim 2010). Hotly debated issues

span all spheres of human activity; from liberal vs. conservative politics, to radical

vs. counter-radical religious debate, to climate change debate in scientific community,

to globalization debate in economics, and to nuclear disarmament debate in

security. Many prominent ’camps’ have emerged within Internet debate rhetoric and

practice (Dahlberg, n.d.).

In this research I utilized feature extraction and model fitting techniques to process

the rhetoric found in the web sites of 23 Indonesian Islamic religious organizations,

later with 26 similar organizations from the United Kingdom to profile their

ideology and activity patterns along a hypothesized radical/counter-radical scale, and

presented an end-to-end system that is able to help researchers to visualize the data

in an interactive fashion on a time line. The subject data of this study is the articles

downloaded from the web sites of these organizations dating from 2001 to 2011,

and in 2013. I developed algorithms to rank these organizations by assigning them

to probable positions on the scale. I showed that the developed Rasch model fits

the data using Andersen’s LR-test (likelihood ratio). I created a gold standard of

the ranking of these organizations through an expertise elicitation tool. Then using

my system I computed expert-to-expert agreements, and then presented experimental

results comparing the performance of three baseline methods to show that the

Rasch model not only outperforms the baseline methods, but it was also the only

system that performs at expert-level accuracy.

I developed an end-to-end system that receives list of organizations from experts,

mines their web corpus, prepare discourse topic lists with expert support, and then

ranks them on scales with partial expert interaction, and finally presents them on an

easy to use web based analytic system.

Contributors

Agent

Created

Date Created
  • 2016