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IEEE 2015 JAVA PROJECT TITLES
S.No / Title1 / Efficient Filtering Algorithms for Location-Aware Publish/Subscribe
Data Mining, April 2015
Demo Link:
2 / Probabilistic Range Query over Uncertain Moving Objects in Constrained Two-Dimensional Space
Data Mining, March 2015
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3 / CrowdOp: Query Optimization for Declarative Crowdsourcing Systems
Data Mining, Aug. 1 2015
Demo Link:
4 / Disease Inference from Health-Related Questions via Sparse Deep Learning
Data Mining, Aug. 1 2015
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5 / Real-Time City-Scale Taxi Ridesharing
Data Mining, July 2015
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6 / t-Closeness through Microaggregation: Strict Privacy with Enhanced Utility Preservation
Data Mining, preprint
7 / RRW - A Robust and Reversible Watermarking Technique for Relational Data
Data Mining, preprint
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8 / Anonymous Two-Factor Authentication in Distributed Systems: Certain Goals Are Beyond Attainment
Dependable and Secure Computing, July-Aug. 1 2015
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9 / A Proximity-aware Interest-clustered P2P File Sharing System
Parallel and Distributed Systems, June 2015
Demo Link:
10 / A Lightweight Secure Scheme for Detecting Provenance Forgery and Packet Drop Attacks in Wireless Sensor Networks
Dependable and Secure Computing, May-June 2015
11 / Secure Data Aggregation Technique for Wireless Sensor Networks in the Presence of Collusion Attacks
Dependable and Secure Computing, Jan Feb 2015
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12 / Secure Spatial Top-k Query Processing via Untrusted Location-Based Service Providers
Dependable and Secure Computing, Jan Feb 2015
13 / Neighbor Similarity Trust against Sybil Attack in P2P E-Commerce
Parallel And Distributed Systems, March 2015
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14 / Secure and Verifiable Policy Update Outsourcing for Big Data Access Control in the Cloud
Parallel and Distributed Systems, Preprint
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15 / Neighbor Discovery in Wireless Networks with Multipacket Reception
Parallel And Distributed Systems, July 2015
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16 / Truthful Greedy Mechanisms for Dynamic Virtual Machine Provisioning and Allocation in Clouds
Parallel And Distributed Systems, February 2015
17 / VMbuddies: Coordinating Live Migration of Multi-Tier Applications in Cloud Environments
Parallel And Distributed Systems, April 2015
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18 / A Hybrid Cloud Approach for Secure Authorized Deduplication
Parallel And Distributed Systems, May 2015
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19 / Path Reconstruction in Dynamic Wireless Sensor Networks Using Compressive Sensing
Networking, Preprint
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20 / Maximizing P2P File Access Availability in Mobile Ad Hoc Networks though Replication for Efficient File Sharing
Computers, April 2015
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21 / Privacy-Preserving and Truthful Detection of Packet Dropping Attacks in Wireless Ad Hoc Networks
Mobile Computing, April 2015
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22 / Tracking Message Spread in Mobile Delay Tolerant Networks
Mobile Computing, Aug 2015
23 / A privacy-preserving framework for managing mobile ad requests and billing information
Mobile Computing, Aug 2015
34 / Towards Large-Scale Histopathological Image Analysis: Hashing-Based Image Retrieval
Image Processing, Feb 2015
25 / Key-Aggregate Searchable Encryption (KASE) for Group Data Sharing via Cloud Storage
Computers, Preprint
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26 / Dynamic Routing for Data Integrity and Delay Differentiated Services in Wireless Sensor Networks
Mobile Computing, Feb 2015
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27 / Privacy-Preserving Ciphertext Multi-Sharing Control for Big Data Storage
Information forensics and security, Aug 2015
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28 / E2R2: Energy-Efficient and Reliable Routing for Mobile Wireless Sensor Networks
Systems Journal, 2015
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29 / Lossless and Reversible Data Hiding in Encrypted Images with Public Key Cryptography
Circuits and Systems for Video Technology, May 2015
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30 / Stochastic Decision Making for Adaptive Crowdsourcing in Medical Big-Data Platforms
Systems, man, and cybernetics, oct 2015
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31 / A Distributed Three-hop Routing Protocol to Increase the Capacity of Hybrid Wireless Networks
Mobile Computing, Preprint
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32 / On Traffic-Aware Partition and Aggregation in MapReduce for Big Data Applications
Parallel And Distributed Systems, Preprint
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IEEE 2015 DOTNET PROJECT TITLES
S.No / Title33 / Control Cloud Data Access Privilege and Anonymity With Fully Anonymous Attribute-Based Encryption
Information Forensics and Security, Jan 2015
34 / Key-Recovery Attacks on KIDS, a Keyed Anomaly Detection System
Dependable and Secure Computing, May Jun 2015
35 / Secure and Distributed Data Discovery and Dissemination in Wireless Sensor Networks
Parallel And Distributed Systems, April 2015
36 / Cost-Effective Authentic and Anonymous Data Sharing with Forward Security
Computers, Preprint
IEEE 2015 TITLE WITH ABSTRACT – JAVA/ J2EE
S.No / Title1 / Efficient Filtering Algorithms for Location-Aware Publish/Subscribe
Data Mining, April 2015
Location-based services have been widely adopted in many systems. Existing works employ a pull model or user-initiated model, where a user issues a query to a server which replies with location-aware answers. To provide users with instant replies, a push model or server-initiated model is becoming an inevitable computing model in the next-generation location-based services. In the push model, subscribers register spatio-textual subscriptions to capture their interests, and publishers post spatio-textual messages. This calls for a high-performance location-aware publish/subscribe system to deliver publishers' messages to relevant subscribers. In this paper, we address the research challenges that arise in designing a location-aware publish/subscribe system. We propose an R-tree based index by integrating textual descriptions into R-tree nodes. We devise efficient filtering algorithms and effective pruning techniques to achieve high performance. Our method can support both conjunctive queries and ranking queries.
2 / Probabilistic Range Query over Uncertain Moving Objects in Constrained Two-Dimensional Space
Data Mining, March 2015
Probabilistic range query (PRQ) over uncertain moving objects has attracted much attentions in recent years. Most of existing works focus on the PRQ for objects moving freely in two-dimensional (2D) space. In contrast, this paper studies the PRQ over objects moving in a constrained 2D space where objects are forbidden to be located in some specific areas. We dub it the constrained space probabilistic range query (CSPRQ). We analyze its unique properties and show that to process the CSPRQ using a straightforward solution is infeasible. The key idea of our solution is to use a strategy called pre-approximation that can reduce the initial problem to a highly simplified version, implying that it makes the rest of steps easy to tackle. In particular, this strategy itself is pretty simple and easy to implement. Furthermore, motivated by the cost analysis, we further optimize our solution. The optimizations are mainly based on two insights: (i) the number of effective subdivisions is no more than 1; and (ii) an entity with the larger span is more likely to subdivide a single region.
3 / CrowdOp: Query Optimization for Declarative Crowdsourcing Systems
Knowledge and Data Engineering, Aug. 1 2015
We study the query optimization problem in declarative crowdsourcing systems. Declarative crowdsourcing is designed to hide the complexities and relieve the user of the burden of dealing with the crowd. The user is only required to submit an SQL-like query and the system takes the responsibility of compiling the query, generating the execution plan and evaluating in the crowdsourcing marketplace. A given query can have many alternative execution plans and the difference in crowdsourcing cost between the best and the worst plans may be several orders of magnitude. Therefore, as in relational database systems, query optimization is important to crowdsourcing systems that provide declarative query interfaces. In this paper, we propose CROWDOP, a cost-based query optimization approach for declarative crowdsourcing systems. CROWDOP considers both cost and latency in query optimization objectives and generates query plans that provide a good balance between the cost and latency. We develop efficient algorithms in the CROWDOP for optimizing three types of queries: selection queries, join queries, and complex selection-join queries.
4 / Disease Inference from Health-Related Questions via Sparse Deep Learning
Knowledge and Data Engineering, Aug. 1 2015
Automatic disease inference is of importance to bridge the gap between what online health seekers with unusual symptoms need and what busy human doctors with biased expertise can offer. However, accurately and efficiently inferring diseases is non-trivial, especially for community-based health services due to the vocabulary gap, incomplete information, correlated medical concepts, and limited high quality training samples. In this paper, we first report a user study on the information needs of health seekers in terms of questions and then select those that ask for possible diseases of their manifested symptoms for further analytic. We next propose a novel deep learning scheme to infer the possible diseases given the questions of health seekers. The proposed scheme is comprised of two key components. The first globally mines the discriminant medical signatures from raw features. The second deems the raw features and their signatures as input nodes in one layer and hidden nodes in the subsequent layer, respectively. Meanwhile, it learns the inter-relations between these two layers via pre-training with pseudo-labeled data. Following that, the hidden nodes serve as raw features for the more abstract signature mining.
5 / Real-Time City-Scale Taxi Ridesharing
Knowledge and Data Engineering, July 2015
Proposed and developed a taxi-sharing system that accepts taxi passengers' real-time ride requests sent from smart phones and schedules proper taxis to pick up them via ride sharing, subject to time, capacity, and monetary constraints. The monetary constraints provide incentives for both passengers and taxi drivers: passengers will not pay more compared with no ride sharing and get compensated if their travel time is lengthened due to ride sharing; taxi drivers will make money for all the detour distance due to ride sharing. While such a system is of significant social and environmental benefit, e.g., saving energy consumption and satisfying people's commute, real-time taxi-sharing has not been well studied yet. To this end, we devise a mobile-cloud architecture based taxi-sharing system. Taxi riders and taxi drivers use the taxi-sharing service provided by the system via a smart phone App. The Cloud first finds candidate taxis quickly for a taxi ride request using a taxi searching algorithm supported by a spatio-temporal index. A scheduling process is then performed in the cloud to select a taxi that satisfies the request with minimum increase in travel distance. We built an experimental platform using the GPS trajectories generated by over 33,000 taxis over a period of three months. A ride request generator is developed (available at in terms of the stochastic process modelling real ride requests learned from the data set.
6 / t-Closeness through Microaggregation: Strict Privacy with Enhanced Utility Preservation
Microaggregation is a technique for disclosure limitation aimed at protecting the privacy of data subjects in microdata releases. It has been used as an alternative to generalization and suppression to generate k-anonymous data sets, where the identity of each subject is hidden within a group of k subjects. Unlike generalization, microaggregation perturbs the data and this additional masking freedom allows improving data utility in several ways, such as increasing data granularity, reducing the impact of outliers and avoiding discretization of numerical data. k-Anonymity, on the other side, does not protect against attribute disclosure, which occurs if the variability of the confidential values in a group of k subjects is too small. To address this issue, several refinements of k-anonymity have been proposed, among which t-closeness stands out as providing one of the strictest privacy guarantees. Existing algorithms to generate t-close data sets are based on generalization and suppression (they are extensions of k-anonymization algorithms based on the same principles). This paper proposes and shows how to use microaggregation to generate k-anonymous t-close data sets. The advantages of microaggregation are analyzed, and then several microaggregation algorithms for k-anonymous t-closeness are presented and empirically evaluated.
7 / Anonymous Two-Factor Authentication in Distributed Systems: Certain Goals Are Beyond Attainment
Dependable and Secure Computing, July-Aug. 1 2015
Despite two decades of intensive research, it remains a challenge to design a practical anonymous two-factor authentication scheme, for the designers are confronted with an impressive list of security requirements (e.g., resistance to smart card loss attack) and desirable attributes (e.g., local password update). Numerous solutions have been proposed, yet most of them are shortly found either unable to satisfy some critical security requirements or short of a few important features. To overcome this unsatisfactory situation, researchers often work around it in hopes of a new proposal (but no one has succeeded so far), while paying little attention to the fundamental question: whether or not there are inherent limitations that prevent us from designing an “ideal” scheme that satisfies all the desirable goals?
8 / A Proximity-aware Interest-clustered P2P File Sharing System
Parallel and Distributed Systems, June 2015
Efficient file query is important to the overall performance of peer-to-peer (P2P) file sharing systems. Clustering peers by their common interests can significantly enhance the efficiency of file query. Clustering peers by their physical proximity can also improve file query performance. However, few current works are able to cluster peers based on both peer interest and physical proximity. Although structured P2Ps provide higher file query efficiency than unstructured P2Ps, it is difficult to realize it due to their strictly defined topologies. In this work, we introduce a Proximity-Aware and Interest-clustered P2P file sharing System (PAIS) based on a structured P2P, which forms physically-close nodes into a cluster and further groups physically-close and common-interest nodes into a sub-cluster based on a hierarchical topology. PAIS uses an intelligent file replication algorithm to further enhance file query efficiency. It creates replicas of files that are frequently requested by a group of physically close nodes in their location. Moreover, PAIS enhances the intra-sub-cluster file searching through several approaches. First, it further classifies the interest of a sub-cluster to a number of sub-interests, and clusters common-sub-interest nodes into a group for file sharing. Second, PAIS builds an overlay for each group that connects lower capacity nodes to higher capacity nodes for distributed file querying while avoiding node overload. Third, to reduce file searching delay, PAIS uses proactive file information collection so that a file requester can know if its requested file is in its nearby nodes. Fourth, to reduce the overhead of the file information collection, PAIS uses bloom filter based file information collection and corresponding distributed file searching. Fifth, to improve the file sharing efficiency, PAIS ranks the bloom filter results in order. Sixth, considering that a recently visited file tends to be visited again, the bloom filter based appr- ach is enhanced by only checking the newly added bloom filter information to reduce file searching delay.
9 / A Lightweight Secure Scheme for Detecting Provenance Forgery and Packet Drop Attacks in Wireless Sensor Networks
Dependable and Secure Computing, May-June 2015
Large-scale sensor networks are deployed in numerous application domains, and the data they collect are used in decision-making for critical infrastructures. Data are streamed from multiple sources through intermediate processing nodes that aggregate information. A malicious adversary may introduce additional nodes in the network or compromise existing ones. Therefore, assuring high data trustworthiness is crucial for correct decision-making. Data provenance represents a key factor in evaluating the trustworthiness of sensor data. Provenance management for sensor networks introduces several challenging requirements, such as low energy and bandwidth consumption, efficient storage and secure transmission. In this paper, we propose a novel lightweight scheme to securely transmit provenance for sensor data. The proposed technique relies on in-packet Bloom filters to encode provenance. We introduce efficient mechanisms for provenance verification and reconstruction at the base station. In addition, we extend the secure provenance scheme with functionality to detect packet drop attacks staged by malicious data forwarding nodes. We evaluate the proposed technique both analytically and empirically, and the results prove the effectiveness and efficiency of the lightweight secure provenance scheme in detecting packet forgery and loss attacks.
10 / Secure Data Aggregation Technique for Wireless Sensor Networks in the Presence of Collusion Attacks
Dependable and Secure Computing, Jan Feb 2015