Zhuojun Duan

A novel contact prediction-based routing scheme for DTNs

Delay/disruption tolerant networks (DTNs) make opportunistic communications by utilising the mobility of nodes. The characteristics of high mobility of nodes and high dynamicity of network topology result in an absence of instantaneous end-to-end path from any source to a destination and thus make routing a challenge in DTNs. To deal with this issue, researchers have investigated a variety of routing schemes for DTNs based on the prediction of future contacts, in which node mobility is explored and used. However, the previous works did not consider the instant contact information such as the last contact duration time and the instant separation time since the last contact whilst making routing decisions, leading to less prediction accuracy of future contacts and thus worse routing performance. In this paper, a novel contact prediction-based routing scheme is proposed for DTNs to increase delivery ratio by considering the instant contact information. Specifically, to predict the contact probability of two nodes accurately, the statistical contact information, the instant contact information and the contact transitivity are comprehensively considered. The simulation evaluations show that the proposed contact prediction-based routing substantially improves delivery ratio and reduces delivery latency and delivery overhead compared with traditional DTN routing schemes. Copyright

Truthful Incentive Mechanisms for Social Cost Minimization in Mobile Crowdsourcing Systems

With the emergence of new technologies, mobile devices are capable of undertaking computational and sensing tasks. A large number of users with these mobile devices promote the formation of the Mobile Crowdsourcing Systems (MCSs). Within a MCS, each mobile device can contribute to the crowdsourcing platform and get rewards from it. In order to achieve better performance, it is important to design a mechanism that can attract enough participants with mobile devices and then allocate the tasks among participants efficiently. In this paper, we are interested in the investigation of tasks allocation and price determination in MCSs. Two truthful auction mechanisms are proposed for different working patterns. A Vickrey–Clarke–Groves (VCG)-based auction mechanism is proposed to the continuous working pattern, and a suboptimal auction mechanism is introduced for the discontinuous working pattern. Further analysis shows that the proposed mechanisms have the properties of individual rationality and computational efficiencies. Experimental results suggest that both mechanisms guarantee all the mobile users bidding with their truthful values and the optimal maximal social cost can be achieved in the VCG-based auction mechanism.

Distributed Auctions for Task Assignment and Scheduling in Mobile Crowdsensing Systems

With the emergence of Mobile Crowdsensing Systems (MCSs), many auction schemes have been proposed to incentivize mobile users to participate in sensing activities. However, in most of the existing work, the heterogeneity of MCSs has not been fully exploited. To tackle this issue, in this paper, we study the joint problem of sensing task assignment and scheduling while considering partial fulfillment, attribute diversity, and price diversity. We first elaborately model the problem as a reverse auction and design a distributed auction framework. Then, based on this framework, we propose two distributed auction schemes, cost-preferred auction scheme (CPAS) and time schedule-preferred auction scheme (TPAS), which differ on the methods of task scheduling, winner determination, and payment computation. We further rigorously prove that both CPAS and TPAS can achieve computational-efficiency, individual-rationality, budget-balance, and truthfulness. Finally, the simulation results validate the effectiveness of both CPAS and TPAS in terms of sensing tasks allocation efficiency, mobile users working time utilization and utility, and truthfulness.

Privacy Issues for Transportation Cyber Physical Systems

Transportation Cyber-Physical Systems (TCPS) developed a lot with the advancement of the transportation industry worldwide. The rapid proliferation of TCPS provides rich data and infinite possibilities for us to analyze and understand the complex inherent mechanism that governs the novel intelligence world. Also, TCPS open a range of new application scenarios, such as vehicular safety, energy efficiency, reduced pollution, and intelligent maintenance services. However, while enjoying the services and convenience provided by TCPS, users, vehicles, and even the systems might lose privacy during information transmission and processing. This chapter summarizes the state-of-art research findings on TCPS in a broad sense. First, we introduce the typical TCPS model and their basic mechanism of data communication. Secondly, considering the privacy issues of TCPS, we give a bird’s-eye view of the up-to-date literature on the problems and privacy protection approaches. Thirdly, we point out the most recently emerging challenges and the potential resolutions for privacy issues in TCPS.

Near-Complete Privacy Protection: Cognitive Optimal Strategy in Location-Based Services

Abstract While enjoying the amenities and convenience provided by the location-based service (LBS) in our daily life, wireless device users may surrender their location together with social activity privacy to the LBS provider. The untrusted LBS server has all the information about users in the LBS and it may track them in various ways or release their privacy data to third parties. To address the privacy protection issue, many location obfuscation algorithms behind a location-privacy preserving mechanisms (LPPMs) were proposed but only approached the trade-off between utility and privacy. Unlike the existing approaches, we propose the first methodology and application, to the best of our knowledge, that enables a near-complete location privacy protection for LBS users to achieve an cognitive optimal resolution by employing the social network associated with the LBS to separate the utility and privacy. Evaluation of both simulation and practical smartphone application shows that the proposed methodology could achieve a near-complete privacy protection in terms of entropy and significantly improve the quality of service utility.

Mining Public Business Knowledge: A Case Study in SEC's EDGAR

Public business information could increase the efficiency and fairness of the securities market for the benefit of investors, corporations, and economy. Since 1934, the U. S. Securities and Exchange Commission (SEC) has required disclosure of all listed companies in forms and documents. SECs EDGAR, which is a data management system of SEC, began to collect electronic documents to help investors get information since 1984. However, although EDGAR provides free access to more than 20 million filings, limited by the function of EDGAR search tools, it is very hard for the end user to query data related to different companies conveniently, not to mention mining the internal knowledge of a large number of documents from the aspect of statistics. Moreover, most of the specific knowledge discovery from documents is still very challenging due to the complexity of material and semantic inside the documents. In this paper, from a case study aspect, we provide a general data extraction and analysis resolution for mining the business knowledge from EDGAR. Our case study particular focuses on mining the annual meeting date of each company which mainly indicated in companys DEF 14A form. We test our resolution with a list of 10,417 companies, more than 98.65% (10,276) companies have been analyzed through our Python scripts automatically, the error is the result of the defect of documents standardization and web mistake. On the whole, 546,451 documents have been scanned and 82,872 annual meeting date records for all 10,417 companies have been extracted and analyzed. The knowledge we mining could be applied to further research and analysis usage. Furthermore, we also provide several general resolution for other researchers to download and analyze other documents such as 10-K, 10-Q et al. All the methods and packages we developed in Python could be applied to many other similar or related applications for further reference. We also release the source code with the library available and license the code under GNU General Public License.

An Edge Correlation Based Differentially Private Network Data Release Method

Differential privacy (DP) provides a rigorous and provable privacy guarantee and assumes adversaries’ arbitrary background knowledge, which makes it distinct from prior work in privacy preserving. However, DP cannot achieve claimed privacy guarantees over datasets with correlated tuples. Aiming to protect whether two individuals have a close relationship in a correlated dataset corresponding to a weighted network, we propose a differentially private network data release method, based on edge correlation, to gain the tradeoff between privacy and utility. Specifically, we first extracted the Edge Profile (PF) of an edge from a graph, which is transformed from a raw correlated dataset. Then, edge correlation is defined based on the PFs of both edges via Jenson-Shannon Divergence (JS-Divergence). Secondly, we transform a raw weighted dataset into an indicated dataset by adopting a weight threshold, to satisfy specific real need and decrease query sensitivity. Furthermore, we propose -correlated edge differential privacy (CEDP), by combining the correlation analysis and the correlated parameter with traditional DP. Finally, we propose network data release (NDR) algorithm based on the -CEDP model and discuss its privacy and utility. Extensive experiments over real and synthetic network datasets show the proposed releasing method provides better utilities while maintaining privacy guarantee.