Minyi Guo

Hierarchical attribute-based encryption and scalable user revocation for sharing data in cloud servers

With rapid development of cloud computing, more and more enterprises will outsource their sensitive data for sharing in a cloud. To keep the shared data confidential against untrusted cloud service providers (CSPs), a natural way is to store only the encrypted data in a cloud. The key problems of this approach include establishing access control for the encrypted data, and revoking the access rights from users when they are no longer authorized to access the encrypted data. This paper aims to solve both problems. First, we propose a hierarchical attribute-based encryption scheme (HABE) by combining a hierarchical identity-based encryption (HIBE) system and a ciphertext-policy attribute-based encryption (CP-ABE) system, so as to provide not only fine-grained access control, but also full delegation and high performance. Then, we propose a scalable revocation scheme by applying proxy re-encryption (PRE) and lazy re-encryption (LRE) to the HABE scheme, so as to efficiently revoke access rights from users.

Flexible Deterministic Packet Marking: An IP Traceback System to Find the Real Source of Attacks

IP traceback is the enabling technology to control Internet crime. In this paper we present a novel and practical IP traceback system called Flexible Deterministic Packet Marking (FDPM) which provides a defense system with the ability to find out the real sources of attacking packets that traverse through the network. While a number of other traceback schemes exist, FDPM provides innovative features to trace the source of IP packets and can obtain better tracing capability than others. In particular, FDPM adopts a flexible mark length strategy to make it compatible to different network environments; it also adaptively changes its marking rate according to the load of the participating router by a flexible flow-based marking scheme. Evaluations on both simulation and real system implementation demonstrate that FDPM requires a moderately small number of packets to complete the traceback process; add little additional load to routers and can trace a large number of sources in one traceback process with low false positive rates. The built-in overload prevention mechanism makes this system capable of achieving a satisfactory traceback result even when the router is heavily loaded. It has been used to not only trace DDoS attacking packets but also enhance filtering attacking traffic.

SAMR: A Self-adaptive MapReduce Scheduling Algorithm in Heterogeneous Environment

Hadoop is seriously limited by its MapReduce scheduler which does not scale well in heterogeneous environment. Heterogenous environment is characterized by various devices which vary greatly with respect to the capacities of computation and communication, architectures, memorizes and power. As an important extension of Hadoop, LATE MapReduce scheduling algorithm takes heterogeneous environment into consideration. However, it falls short of solving the crucial problem – poor performance due to the static manner in which it computes progress of tasks. Consequently, neither Hadoop nor LATE schedulers are desirable in heterogeneous environment. To this end, we propose SAMR: a Self-Adaptive MapReduce scheduling algorithm, which calculates progress of tasks dynamically and adapts to the continuously varying environment automatically. When a job is committed, SAMR splits the job into lots of fine-grained map and reduce tasks, then assigns them to a series of nodes. Meanwhile, it reads historical information which stored on every node and updated after every execution. Then, SAMR adjusts time weight of each stage of map and reduce tasks according to the historical information respectively. Thus, it gets the progress of each task accurately and finds which tasks need backup tasks. What’s more, it identifies slow nodes and classifies them into the sets of slow nodes dynamically. According to the information of these slow nodes, SAMR will not launch backup tasks on them, ensuring the backup tasks will not be slow tasks any more. It gets the final results of the fine-grained tasks when either slow tasks or backup tasks finish first. The proposed algorithm is evaluated by extensive experiments over various heterogeneous environment. Experimental results show that SAMR significantly decreases the time of execution up to 25% compared with Hadoop’s scheduler and up to 14% compared with LATE scheduler.

TASA: Tag-Free Activity Sensing Using RFID Tag Arrays

Radio Frequency IDentification (RFID) has attracted considerable attention in recent years for its low cost, general availability, and location sensing functionality. Most existing schemes require the tracked persons to be labeled with RFID tags. This requirement may not be satisfied for some activity sensing applications due to privacy and security concerns and uncertainty of objects to be monitored, e.g., group behavior monitoring in warehouses with privacy limitations, and abnormal customers in banks. In this paper, we propose TASA-Tag-free Activity Sensing using RFID tag Arrays for location sensing and frequent route detection. TASA relaxes the monitored objects from attaching RFID tags, online recovers and checks frequent trajectories by capturing the Received Signal Strength Indicator (RSSI) series for passive RFID tag arrays where objects traverse. In order to improve the accuracy for estimated trajectories and accelerate location sensing, TASA introduces reference tags with known positions. With the readings from reference tags, TASA can locate objects more accurately. Extensive experiment shows that TASA is an effective approach for certain activity sensing applications.

A class-feature-centroid classifier for text categorization

Automated text categorization is an important technique for many web applications, such as document indexing, document filtering, and cataloging web resources. Many different approaches have been proposed for the automated text categorization problem. Among them, centroid-based approaches have the advantages of short training time and testing time due to its computational efficiency. As a result, centroid-based classifiers have been widely used in many web applications. However, the accuracy of centroid-based classifiers is inferior to SVM, mainly because centroids found during construction are far from perfect locations. We design a fast Class-Feature-Centroid (CFC) classifier for multi-class, single-label text categorization. In CFC, a centroid is built from two important class distributions: inter-class term index and inner-class term index. CFC proposes a novel combination of these indices and employs a denormalized cosine measure to calculate the similarity score between a text vector and a centroid. Experiments on the Reuters-21578 corpus and 20-newsgroup email collection show that CFC consistently outperforms the state-of-the-art SVM classifiers on both micro-F1 and macro-F1 scores. Particularly, CFC is more effective and robust than SVM when data is sparse.

Adaptive location updates for mobile sinks in wireless sensor networks

Mobile sinks can be used to balance energy consumption for sensor nodes in Wireless Sensor Networks (WSNs). Mobile sinks are required to inform sensor nodes about their new location information whenever necessary. However, frequent location updates from mobile sinks can lead to both rapid energy consumption of sensor nodes and increased collisions in wireless transmissions. We propose a new solution with adaptive location updates for mobile sinks to resolve this problem. When a sink moves, it only needs to broadcast its location information within a local area other than among the entire network. Both theoretical analysis and simulation studies show that this solution consumes less energy in each sensor node and also decreases collisions in wireless transmissions, which can be used in large-scale WSNs.

Supervised hashing with latent factor models

Due to its low storage cost and fast query speed, hashing has been widely adopted for approximate nearest neighbor search in large-scale datasets. Traditional hashing methods try to learn the hash codes in an unsupervised way where the metric (Euclidean) structure of the training data is preserved. Very recently, supervised hashing methods, which try to preserve the semantic structure constructed from the semantic labels of the training points, have exhibited higher accuracy than unsupervised methods. In this paper, we propose a novel supervised hashing method, called latent factor hashing(LFH), to learn similarity-preserving binary codes based on latent factor models. An algorithm with convergence guarantee is proposed to learn the parameters of LFH. Furthermore, a linear-time variant with stochastic learning is proposed for training LFH on large-scale datasets. Experimental results on two large datasets with semantic labels show that LFH can achieve superior accuracy than state-of-the-art methods with comparable training time.

Joint Optimization of Lifetime and Transport Delay under Reliability Constraint Wireless Sensor Networks

This paper first presents an analysis strategy to meet requirements of a sensing application through trade-offs between the energy consumption (lifetime) and source-to-sink transport delay under reliability constraint wireless sensor networks. A novel data gathering protocol named Broadcasting Combined with Multi-NACK/ACK (BCMN/A) protocol is proposed based on the analysis strategy. The BCMN/A protocol achieves energy and delay efficiency during the data gathering process both in intra-cluster and inter-cluster. In intra-cluster, after each round of TDMA collection, a cluster head broadcasts NACK to indicate nodes which fail to send data in order to prevent nodes that successfully send data from retransmission. The energy for data gathering in intra-cluster is conserved and transport delay is decreased with multi-NACK mechanism. Meanwhile in inter-clusters, multi-ACK is returned whenever a sensor node sends any data packet. Although the number of ACKs to be sent is increased, the number of data packets to be retransmitted is significantly decreased so that consequently it reduces the node energy consumption. The BCMN/A protocol is evaluated by theoretical analysis as well as extensive simulations and these results demonstrate that our proposed protocol jointly optimizes the network lifetime and transport delay under network reliability constraint.

Manhattan hashing for large-scale image retrieval

Hashing is used to learn binary-code representation for data with expectation of preserving the neighborhood structure in the original feature space. Due to its fast query speed and reduced storage cost, hashing has been widely used for efficient nearest neighbor search in a large variety of applications like text and image retrieval. Most existing hashing methods adopt Hamming distance to measure the similarity (neighborhood) between points in the hashcode space. However, one problem with Hamming distance is that it may destroy the neighborhood structure in the original feature space, which violates the essential goal of hashing. In this paper, Manhattan hashing (MH), which is based on Manhattan distance, is proposed to solve the problem of Hamming distance based hashing. The basic idea of MH is to encode each projected dimension with multiple bits of natural binary code (NBC), based on which the Manhattan distance between points in the hashcode space is calculated for nearest neighbor search. MH can effectively preserve the neighborhood structure in the data to achieve the goal of hashing. To the best of our knowledge, this is the first work to adopt Manhattan distance with NBC for hashing. Experiments on several large-scale image data sets containing up to one million points show that our MH method can significantly outperform other state-of-the-art methods.

RARE: An Energy-Efficient Target Tracking Protocol for Wireless Sensor Networks

Energy efficiency for target tracking in wireless sensor networks is very important and can be improved by reducing the number of nodes involved in communications. We propose two algorithms, RARE-area and RARE-node to reduce the number of nodes participating in tracking and so increase energy efficiency. The RARE-area algorithm ensures that only nodes that receive a given quality of data participate in tracking and the RARE-node algorithm ensures that any nodes with redundant information do not participate in tracking. Simulation studies show significant energy savings are obtained with implementation of either the RARE-area algorithm alone or both RARE-area and RARE-node algorithms together.