Image Processing 2016-15

IEEE 2016- A LOCALITY SENSITIVE LOW-RANK MODEL FOR IMAGE TAG COMPLETION

Abstract: Many visual applications have benefited from the outburst of web images, yet the imprecise and incomplete tags arbitrarily provided by users, as the thorn of the   rose, may hamper the performance of retrieval or indexing systems relying on such data. In this paper, we propose a novel locality sensitive low-rank model for image tag  completion, which approximates the global nonlinear model with a collection of local linear models. To effectively infuse the idea of locality sensitivity, a simple and effective pre-processing module is designed to learn suitable representation for data partition, and a global consensus regularizer is introduced to mitigate the risk of overfitting. Meanwhile, low-rank matrix factorization is employed as local models, where the local geometry structures are preserved for the low-dimensional representation of both tags and samples. Extensive empirical evaluations conducted on three datasets demonstrate the effectiveness and efficiency of the proposed method, where our method outperforms pervious ones by a large margin.

IEEE 2016- A SHOULDER SURFING RESISTANT GRAPHICAL AUTHENTICATION SYSTEM

Abstract: Authentication based on passwords is used largely in applications for computer security and privacy. However, human actions such as choosing bad passwords and inputting passwords in an insecure way are regarded as ”the weakest link” in the authentication chain. Rather than arbitrary alphanumeric strings, users tend to choose passwords either short or meaningful for easy memorization. With web applications and mobile apps piling up, people can access these applications anytime and anywhere with various devices. This evolution brings great convenience but also increases the probability of exposing passwords to shoulder surfing attacks. Attackers can observe directly or use external recording devices to collect users’ credentials. To overcome this problem, we proposed a novel authentication system PassMatrix, based on graphical passwords to resist shoulder surfing attacks. With a one-time valid login indicator and circulative horizontal and vertical bars covering the entire scope of pass-images, PassMatrix offers no hint for attackers to figure out or narrow down the password even they conduct multiple camera-based attacks. We also implemented a PassMatrix prototype on Android and carried out real user experiments to evaluate its memorability and usability. From the experimental result, the proposed system achieves better resistance to shoulder surfing attacks while maintaining usability.

IEEE 2016- ENHANCING SKETCH-BASED IMAGE RETRIEVAL BY RE-RANKING AND RELEVANCE FEEDBACK

Abstract: Sketch-based image retrieval often needs to optimize the tradeoff between efficiency and precision. Index structures are typically applied to large-scale databases to realize efficient retrievals. However, the performance can be affected by quantization errors. Moreover, the ambiguousness of user-provided examples may also degrade the performance, when compared with traditional image retrieval methods. Sketch-based image retrieval systems that preserve the index structure are challenging. In this paper, we propose an effective sketch-based image retrieval approach with re-ranking and relevance feedback schemes. Our approach makes full use of the semantics in query sketches and the top ranked images of the initial results. We also apply relevance feedback to find more relevant images for the input query sketch. The integration of the two schemes results in mutual benefits and improves the performance of the sketch-based image retrieval.

Data Mining IEEE 2016

IEEE 2016-A COMPREHENSIVE STUDY ON WILLINGNESS MAXIMIZATION FOR SOCIAL ACTIVITY PLANNING WITH QUALITY GUARANTEE

Abstract: Studies show that a person is willing to join a social group activity if the activity is interesting, and if some close friends also join the activity as companions. The literature has demonstrated that the interests of a person and the social tightness among friends can be effectively derived and mined from social networking websites. However, even with the above two kinds of  information widely available, social group activities still need to be coordinated manually, and the process is tedious and time-consuming for users, especially for a large social group activity, due to complications of social connectivity and the diversity of possible interests among friends. To address the above important need, this paper proposes to automatically select and recommend potential attendees of a social group activity, which could be very useful for social networking websites as a value-added service. We first formulate a new problem, named Willingness  mAximization for Social grOup (WASO). This paper points out that the solution obtained by a greedy algorithm is likely to be trapped in a local optimal solution. Thus, we design a new randomized algorithm to effectively and efficiently solve the problem. Given the available computational budgets, the proposed algorithm is able to optimally allocate the resources and find a solution with an approximation ratio. We implement the proposed algorithm in Facebook, and the user study demonstrates that social groups obtained by the proposed algorithm significantly outperform the solutions manually configured by users.

 IEEE 2016-CRAWLING HIDDEN OBJECTS WITH KNN QUERIES

Abstract: Many websites offering Location Based Services (LBS) provide a kNN search interface that returns the top-k nearest neighbor objects (e.g., nearest restaurants) for a given query location. This paper addresses the problem of crawling all objects efficiently from an LBS website, through the public kNN web search interface it provides. Specifically, we develop crawling algorithm for 2D and higher-dimensional spaces, respectively, and demonstrate through theoretical analysis that the overhead of our algorithms can be bounded by a function of the number of dimensions and the number of crawled objects, regardless of the underlying distributions of the objects. We also extend the algorithms to leverage scenarios where certain auxiliary information about the underlying data distribution, e.g., the population density of an area which is often positively correlated with the density of LBS objects, is available. Extensive experiments on real-world datasets demonstrate the superiority of our algorithms over the state-of-the-art competitors in the literature.

IEEE 2016- EFFICIENT AND EXACT LOCAL SEARCH FOR RANDOM WALK BASED TOP-K PROXIMITY QUERY IN LARGE GRAPHS

Abstract: Top-k proximity query in large graphs is a fundamental problem with a wide range of applications. Various random walk based measures have been proposed to measure the proximity between different nodes. Although these measures are effective,

efficiently computing them on large graphs is a challenging task. In this paper, we develop an efficient and exact local search method, FLoS (Fast Local Search), for top-k proximity query in large graphs. FLoS guarantees the exactness of the solution. Moreover, it can be applied to a variety of commonly used proximity measures. FLoS is based on the no local optimum property of proximity measures. We show that many measures have no local optimum. Utilizing this property, we introduce several operations to manipulate transition probabilities and develop tight lower and upper bounds on the proximity values. The lower and upper bounds monotonically converge to the exact proximity value when more nodes are visited.We further extend FLoS to measures having local optimum by utilizing relationship among different measures. We perform comprehensive experiments on real and synthetic large graphs to evaluate the efficiency and effectiveness of the proposed method.

IEEE 2016- EFFICIENT R-TREE BASED INDEXING SCHEME FOR SERVER-CENTRIC CLOUD STORAGE SYSTEM

Abstract: Cloud storage system poses new challenges to the community to support efficient concurrent querying tasks for various data-intensive applications, where indices always hold important positions. In this paper, we explore a practical method to construct a two-layer indexing scheme for multi-dimensional data in diverse server-centric cloud storage system. We first propose RT-HCN, an indexing scheme integrating R-tree based indexing structure and HCN-based routing protocol. RT-HCN organizes storage and compute nodes into an HCN overlay, one of the newly proposed sever-centric data center topologies. Based on the properties of HCN, we design a specific index mapping technique to maintain layered global indices and corresponding query processing algorithms to support efficient query tasks. Then, we expand the idea of RT-HCN onto another server-centric data center topology DCell, discovering a potential generalized and feasible way of deploying two-layer indexing schemes on other server-centric networks. Furthermore, we prove theoretically that RT-HCN is both space-efficient and query-efficient, by which each node actually maintains a tolerable number of global indices while high concurrent queries can be processed within accepted overhead. We finally conduct targeted experiments on Amazon’s EC2 platforms, comparing our design with RT-CAN, a similar indexing scheme for traditional P2P network. The results validate the query efficiency, especially the speedup of point query of RT-HCN, depicting its potential applicability in future data centers.

IEEE 2016- RSKNN: KNN SEARCH ON ROAD NETWORKS BY INCORPORATING SOCIAL INFLUENCE

Abstract: Although kNN search on a road network Gr, i.e., finding k nearest objects to a query user q on Gr, has been extensively studied, existing works neglected the fact that the q’s social information can play an important role in this kNN query. Many real-world

applications, such as location-based social networking services, require such a query. In this paper, we study a new problem: Knn search on road networks by incorporating social influence (RSkNN). Specifically, the state-of-the-art Independent Cascade (IC) model in social network is applied to define social influence. One critical challenge of the problem is to speed up the computation of the social influence over large road and social networks. To address this challenge, we propose three efficient index-based search algorithms, i.e., road network-based (RN-based), social network-based (SN-based), and hybrid indexing algorithms. In the RN-based algorithm, we employ a filtering-and-verification framework for tackling the hard problem of computing social influence. In the SN-based algorithm, we embed social cuts into the index, so that we speed up the query. In the hybrid algorithm, we propose an index, summarizing the road and social networks, based on which we can obtain query answers efficiently. Finally, we use real road and social network data to empirically verify the efficiency and efficacy of our solutions.

IEEE 2016- NATERGM: A MODEL FOR EXAMINING THE ROLE OF NODAL ATTRIBUTES IN DYNAMIC SOCIAL MEDIA NETWORKS

Abstract: Social media networks are dynamic. As such, the order in which network ties develop is an important aspect of the network dynamics. This study proposes a novel dynamic network model, the Nodal Attribute-based Temporal Exponential Random Graph Model (NATERGM) for dynamic network analysis. The proposed model focuses on how the nodal attributes of a network affect the order in which the network ties develop. Temporal patterns in social media networks are modeled based on the nodal attributes of individuals and the time information of network ties. Using social media data collected from a knowledge sharing community, empirical tests were conducted to evaluate the performance of the NATERGM on identifying the temporal patterns and predicting the characteristics of the future networks. Results showed that the NATERGM demonstrated an enhanced pattern testing capability and an increased prediction accuracy of network characteristics compared to benchmark models. The proposed NATERGM model helps explain the roles of nodal attributes in the formation process of dynamic networks.

IEEE 2016- RESOLVING MULTI-PARTY PRIVACY CONFLICTS IN SOCIAL MEDIA

Abstract: Items shared through Social Media may affect more than one user’s privacy—e.g., photos that depict multiple users, comments that mention multiple users, events in which multiple users are invited, etc. The lack of multi-party privacy management support in current mainstream Social Media infrastructures makes users unable to appropriately control to whom these items are actually shared or not. Computational mechanisms that are able to merge the privacy preferences of multiple users into a single policy for an item can help solve this problem. However, merging multiple users’ privacy preferences is not an easy task, because privacy preferences may conflict, so methods to resolve conflicts are needed. Moreover, these methods need to consider how users’ would

actually reach an agreement about a solution to the conflict in order to propose solutions that can be acceptable by all of the users affected by the item to be shared. Current approaches are either too demanding or only consider fixed ways of aggregating privacy

preferences. In this paper, we propose the first computational mechanism to resolve conflicts for multi-party privacy management in Social Media that is able to adapt to different situations by modelling the concessions that users make to reach a solution to the conflicts. We also present results of a user study in which our proposed mechanism outperformed other existing approaches in terms of how many times each approach matched users’ behavior.

Cloud Computing-IEEE 2016

IEEE 2016-CONDITIONAL IDENTITY-BASED BROADCAST PROXY RE-ENCRYPTION AND ITS APPLICATION TO CLOUD EMAIL

Cloud Computing

ABSTRACT: Recently, a number of extended Proxy Re-Encryptions (PRE), e.g. Conditional (CPRE), identity-based PRE (IPRE) and broadcast PRE (BPRE), have been proposed for flexible applications. By incorporating CPRE, IPRE and BPRE, this paper proposes a versatile primitive referred to as conditional identity-based broadcast PRE (CIBPRE) and formalizes its semantic security. CIBPRE allows a sender to encrypt a message to multiple receivers by specifying these receivers’ identities, and the sender can delegate a re-encryption key to a proxy so that he can convert the initial ciphertext into a new one to a new set of intended receivers. Moreover, the re-encryption key can be associated with a condition such that only the matching ciphertexts can be re-encrypted, which allows the original sender to enforce access control over his remote ciphertexts in a fine-grained manner. We propose an efficient CIBPRE scheme with provable security. In the instantiated scheme, the initial ciphertext, the re-encrypted ciphertext and the re-encryption key are all in constant size, and the parameters to generate a re-encryption key are independent of the original receivers of any initial ciphertext. Finally, we show an application of our CIBPRE to secure cloud email system advantageous over existing secure email systems based on Pretty Good Privacy protocol or identity-based encryption.

IEEE 2016-DYNAMIC AND PUBLIC AUDITING WITH FAIR ARBITRATION FOR CLOUD DATA

Cloud Computing

Abstract: Cloud users no longer physically possess their data, so how to ensure the integrity of their outsourced data becomes a challenging task.  Moreover, threat models in these schemes usually assume an honest data owner and focus on detecting a dishonest cloud service provider despite the fact that clients may also misbehave.  This paper proposes a public auditing scheme with data dynamics support and fairness arbitration of potential disputes.  In particular, we design an index switcher to eliminate the limitation of index usage in tag computation in current schemes and achieve efficient handling of data dynamics.

IEEE 2016- ENABLING FINE-GRAINED MULTI-KEYWORD SEARCH SUPPORTING CLASSIFIED SUB-DICTIONARIES OVER ENCRYPTED CLOUD DATA

Cloud Computing

Abstract: Using cloud computing, individuals can store their data on remote servers and allow data access to public users through the cloud servers. As the outsourced data are likely to contain sensitive privacy information, they are typically encrypted before uploaded to the cloud. This, however, significantly limits the usability of outsourced data due to the difficulty of searching over the encrypted data. In this paper, we address this issue by developing the fine-grained multi-keyword search schemes over encrypted cloud data. Our original contributions are three-fold. First, we introduce the relevance scores and preference factors upon keywords which enable the precise keyword search and personalized user experience. Second, we develop a practical and very efficient multi-keyword search scheme. The proposed scheme can support complicated logic search the mixed “AND”, “OR” and “NO” operations of keywords. Third, we further employ the classified sub-dictionaries technique to achieve better efficiency on index building, trapdoor generating and query. Lastly, we analyze the security of the proposed schemes in terms of confidentiality of documents, privacy protection of index and trapdoor, and unlinkability of trapdoor. Through extensive experiments using the real-world dataset, we validate the performance of the proposed schemes. Both the security analysis and experimental results demonstrate that the proposed schemes can achieve the same security level comparing to the existing ones and better performance in terms of functionality, query complexity and efficiency.

IEEE 2016- PROVIDING USER SECURITY GUARANTEES IN PUBLIC INFRASTRUCTURE CLOUDS

Cloud Computing

Abstract: The infrastructure cloud (IaaS) service model offers improved resource flexibility and availability, where tenants – insulated from the minutiae of hardware maintenance – rent computing resources to deploy and operate complex systems. Large-scale services running on IaaS platforms demonstrate the viability of this model; nevertheless, many organizations operating on sensitive data avoid migrating operations to IaaS platforms due to security concerns. In this paper, we describe a framework for data and operation security in IaaS, consisting of protocols for a trusted launch of virtual machines and domain-based storage protection. We continue with an extensive theoretical analysis with proofs about protocol resistance against attacks in the defined threat model. The protocols allow trust to be established by remotely attesting host platform configuration prior to launching guest virtual machines and ensure confidentiality of data in remote storage, with encryption keys maintained outside of the IaaS domain. Presented experimental results demonstrate the validity and efficiency of the proposed protocols. The framework prototype was implemented on a test bed operating a public electronic health record system, showing that the proposed protocols can be integrated into existing cloud environments. 

IEEE 2016-CLOUD WORKFLOW SCHEDULING WITH DEADLINES AND TIME SLOT AVAILABILITY

Cloud Computing

Abstract: Allocating service capacities in cloud computing is based on the assumption that they are unlimited and can be used at any time. However, available service capacities change with workload and cannot satisfy users’ requests at any time from the cloud provider’s perspective because cloud services can be shared by multiple tasks. Cloud service providers provide available time slots for new user’s requests based on available capacities. In this paper, we consider workflow scheduling with deadline and time slot availability in cloud computing. An iterated heuristic framework is presented for the problem under study which mainly consists of initial solution construction, improvement, and perturbation. Three initial solution construction strategies, two greedy- and fair-based improvement strategies and a perturbation strategy are proposed. Different strategies in the three phases result in several heuristics. Experimental results show that different initial solution and improvement strategies have different effects on solution qualities.

IEEE 2016-CLOUDARMOR: SUPPORTING REPUTATION-BASED TRUST MANAGEMENT FOR CLOUD SERVICES

Cloud Computing

Trust management is one of the most challenging issues for the adoption and growth of cloud computing. The highly dynamic, distributed, and non-transparent nature of cloud services introduces several challenging issues such as privacy, security, and availability. Preserving consumers’ privacy is not an easy task due to the sensitive information involved in the interactions between consumers and the trust management service. Protecting cloud services against their malicious users (e.g., such users might give misleading feedback to disadvantage a particular cloud service) is a difficult problem. Guaranteeing the availability of the trust management service is another significant challenge because of the dynamic nature of cloud environments. In this article, we describe the design and implementation of CloudArmor, a reputation-based trust management framework that provides a set of functionalities to deliver Trust as a Service (TaaS), which includes i) a novel protocol to prove the credibility of trust feedbacks and preserve users’ privacy, ii) an adaptive and robust credibility model for measuring the credibility of trust feedbacks to protect cloud services from malicious users and to compare the trustworthiness of cloud services, and iii) an availability model to manage the availability of the decentralized implementation of the trust management service. The feasibility and benefits of our approach have been validated by a prototype and experimental studies using a collection of real-world trust feedbacks on cloud services.