Neighborhood Discriminant Hashing for Large-Scale Image Retrieval

Neighborhood Discriminant Hashing for Large-Scale Image Retrieval With the proliferation of large-scale community contributed images, hashing-based approximate nearest neighbor search in huge databases has aroused considerable interest from the fields of computer vision and multimedia in recent years because of its computational and memory efficiency. In this paper, we propose a novel hashing method named neighborhood discriminant hashing (NDH) (for short) to implement approximate similarity search. Different from the previous work, we propose to learn a discriminant hashing function by exploiting local discriminative information, i.e., the labels of a sample…

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Lossless and Reversible Data Hiding in Encrypted Images with Public Key Cryptography

Lossless and Reversible Data Hiding in Encrypted Images with Public Key Cryptography Circuits and Systems for Video Technology A lossless, a reversible, and a combined data hiding schemes is proposed for ciphertext images encrypted by public key cryptosystems with probabilistic and homomorphic properties. In the lossless scheme, the ciphertext pixels are replaced with new values to embed the additional data into several LSB-planes of ciphertext pixels by multi-layer wet paper coding. Then, the embedded data can be directly extracted from the encrypted domain, and the data embedding operation does not…

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FastRAQ: A Fast Approach to Range-Aggregate Queries in Big Data Environments

FastRAQ: A Fast Approach to Range-Aggregate Queries in Big Data Environments Range-aggregate queries are to apply a certain aggregate function on all tuples within given query ranges. Existing approaches to range-aggregate queries are insufficient to quickly provide accurate results in big data environments. FastRAQ—a fast approach to range-aggregate queries is proposed in big data environments. FastRAQ first divides big data into independent partitions with a balanced partitioning algorithm, and then generates a local estimation sketch for each partition. When a range-aggregate query request arrives, FastRAQ obtains the result directly by…

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Providing Privacy-Aware Incentives in Mobile Sensing Systems

Providing Privacy-Aware Incentives in Mobile Sensing Systems Mobile sensing relies on data contributed by users through their mobile device (e.g., smart phone) to obtain useful information about people and their surroundings. However, users may not want to contribute due to lack of incentives and concerns on possible privacy leakage. To effectively promote user participation, both incentive and privacy issues should be addressed. Although incentive and privacy have been addressed separately in mobile sensing, it is still an open problem to address them simultaneously. In this paper, we propose two credit-based…

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SPE: Security and Privacy Enhancement Framework for Mobile Devices

SPE: Security and Privacy Enhancement Framework for Mobile Devices A security and privacy enhancement (SPE) framework for unmodified mobile operating systems. SPE introduces a new layer between the application and the operating system and does not require a device be jailbroken or utilize a custom operating system. We utilize an existing ontology designed for enforcing security and privacy policies on mobile devices to build a policy that is customizable. Based on this policy, SPE provides enhancements to native controls that currently exist on the platform for privacy and security sensitive…

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User privacy and data trustworthiness in mobile crowd sensing

User privacy and data trustworthiness in mobile crowd sensing Smartphones and other trendy mobile wearable devices are rapidly becoming the dominant sensing, computing and communication devices in peoples’ daily lives. Mobile crowd sensing is an emerging technology based on the sensing and networking capabilities of such mobile wearable devices. MCS has shown great potential in improving peoples’ quality of life, including healthcare and transportation, and thus has found a wide range of novel applications. However, user privacy and data trustworthiness are two critical challenges faced by MCS. In this article,…

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Privacy-Preserving Relative Location Based Services for Mobile Users

Privacy-Preserving Relative Location Based Services for Mobile Users Location-aware applications have been used widely with the assistance of the latest positioning features in Smart Phone such as GPS, AGPS, etc. However, all the existing applications gather users’ geographical data and transfer them into the pertinent information to give meaning and value. For this kind of solutions, the user’s privacy and security issues might be raised because the geographical location has to be exposed to the service provider. A novel and practical solution is proposed in this article to provide the…

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Energy-Efficient Fault-Tolerant Data Storage and Processing in Mobile Cloud

Energy-Efficient Fault-Tolerant Data Storage and Processing in Mobile Cloud Despite the advances in hardware for hand-held mobile devices, resource-intensive applications (e.g., video and image storage and processing or map-reduce type) still remain off bounds since they require large computation and storage capabilities. Recent research has attempted to address these issues by employing remote servers, such as clouds and peer mobile devices. For mobile devices deployed in dynamic networks (i.e., with frequent topology changes because of node failure/unavailability and mobility as in a mobile cloud), however, challenges of reliability and energy…

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A Location- and Diversity-aware News Feed System for Mobile Users

A Location- and Diversity-aware News Feed System for Mobile Users Android Project A location-aware news feed (LANF) system generates news feeds for a mobile user based on her spatial preference and non-spatial preference. Existing LANF systems simply send the most relevant geo-tagged messages to their users. Unfortunately, the major limitation of such an existing approach is that, a news feed may contain messages related to the same location (i.e., point-of-interest) or the same category of locations (e.g., food, entertainment or sport). Diversity is a very important feature for location-aware news…

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Subspace Learning and Imputation for Streaming Big Data Matrices and Tensors

Subspace Learning and Imputation for Streaming Big Data Matrices and Tensors Extracting latent low-dimensional structure from high-dimensional data is of paramount importance in timely inference tasks encountered with “Big Data” analytics. However, increasingly noisy, heterogeneous, and incomplete datasets, as well as the need for real-time processing of streaming data, pose major challenges to this end. In this context, the present paper permeates benefits from rank minimization to scalable imputation of missing data, via tracking low-dimensional subspaces and unraveling latent (possibly multi-way) structure from incomplete streaming data. For low-rank matrix data,…

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