machine learning projects

machine learning projects

 

Cloud-Assisted Data Fusion and Sensor Selection for Internet-of-Things

Cloud-Assisted Data Fusion and Sensor Selection for Internet-of-Things The Internet of Things (IoT) is connecting people and smart devices on a scale that was once unimaginable. One major challenge for the IoT is to handle vast amount of sensing data generated from the smart devices that are resource-limited and subject to missing data due to link or node failures. By exploring cloud computing with the IoT, we present a cloud-based solution that takes into account the link quality and spatio-temporal correlation of data to minimize energy consumption by selecting sensors…

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Privacy-preserving Verifiable Set Operation in Big Data for Cloud-assisted Mobile Crowdsourcing

Privacy-preserving Verifiable Set Operation in Big Data for Cloud-assisted Mobile Crowdsourcing The ubiquity of smartphones makes the mobile crowdsourcing possible, where the requester can crowdsource data from the workers by using their sensor-rich mobile devices. However, data collection, data aggregation, and data analysis have become challenging problems for a resource constrained requester when data volume is extremely large, i.e., big data. In particular to data analysis, set operations, including intersection, union, and complementation, exist in most big data analysis for filtering redundant data and preprocessing raw data. Facing challenges in…

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Security, Privacy & Incentive Provision for Mobile Crowd Sensing Systems

Security, Privacy & Incentive Provision for Mobile Crowd Sensing Systems Recent advances in sensing, computing, and networking have paved the way for the emerging paradigm of Mobile Crowd Sensing (MCS). The openness of such systems and the richness of data MCS users are expected to contribute to them raise significant concerns for their security, privacy preservation and resilience. Prior works addressed different aspects of the problem. But in order to reap the benefits of this new sensing paradigm, we need a holistic solution. That is, a secure and accountable MCS…

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A Secure and Efficient ID-Based Aggregate Signature Scheme for Wireless Sensor Networks

A Secure and Efficient ID-Based Aggregate Signature Scheme for Wireless Sensor Networks DOWNLOAD PROJECT SYNOPSIS – JAVA DOWNLOAD PROJECT SYNOPSIS – NS2 Affording secure and efficient big data aggregation methods is very attractive in the field of wireless sensor networks research. In real settings, the wireless sensor networks have been broadly applied, such as target tracking and environment remote monitoring. However, data can be easily compromised by a vast of attacks, such as data interception and data tampering, etc. Mainly focus on data integrity protection, give an identity-based aggregate signature…

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Privacy-Preserving Ride Sharing Scheme for Autonomous Vehicles in Big Data Era

Privacy-Preserving Ride Sharing Scheme for Autonomous Vehicles in Big Data Era DOWNLOAD PROJECT SYNOPSIS Ride sharing can reduce the number of vehicles in the streets by increasing the occupancy of vehicles, which can facilitate traffic and reduce crashes and the number of needed parking slots. Autonomous Vehicles (AVs) can make ride sharing convenient, popular, and also necessary because of the elimination of the driver effort and the expected high cost of the vehicles. However, the organization of ride sharing requires the users to disclose sensitive detailed information not only on…

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BLITHE: Behavior Rule Based Insider Threat Detection for Smart Grid

BLITHE: Behavior Rule Based Insider Threat Detection for Smart Grid DOWNLOAD PROJECT SYNOPSIS A behavior rule-based methodology is proposed for insider threat (BLITHE) detection of data monitor devices in smart grid, where the continuity and accuracy of operations are of vital importance. Based on the dc power flow model and state estimation model, three behavior rules are extracted to depict the behavior norms of each device, such that a device (trustee) that is being monitored on its behavior can be easily checked on the deviation from the behavior specification. Specifically,…

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Optimizing Cloud-Based Video Crowdsensing

Optimizing Cloud-Based Video Crowdsensing Wearable and mobile devices are widely used for crowdsensing, as they come with many sensors and are carried everywhere. Among the sensing data, videos annotated with temporal-spatial metadata contain huge amount of information, but consume too much precious storage space. The problem of optimizing cloud-based video crowdsensing in three steps is studied. First, we study the optimal transcoding problem on wearable and mobile cameras. An algorithm to optimally select the coding parameters is proposed to fit more videos at higher quality on wearable and mobile cameras.…

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EPLQ: Efficient Privacy-Preserving Location-Based Query Over Outsourced Encrypted Data

EPLQ: Efficient Privacy-Preserving Location-Based Query Over Outsourced Encrypted Data   With the pervasiveness of smart phones, location based services (LBS) have received considerable attention and become more popular and vital recently. However, the use of LBS also poses a potential threat to user’s location privacy. Aiming at spatial range query, a popular LBS providing information about POIs (Points Of Interest) within a given distance, we present an efficient and privacy-preserving location based query solution, called EPLQ. Specifically, to achieve privacy preserving spatial range query, we propose the first predicate only…

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