Tao Peng

Bio: Tao Peng is an academic researcher from Guangzhou University. The author has contributed to research in topics: Encryption & Cloud computing. The author has an hindex of 13, co-authored 32 publications receiving 655 citations. Previous affiliations of Tao Peng include Central South University.

Enabling Verifiable and Dynamic Ranked Search Over Outsourced Data

Abstract: Cloud computing as a promising computing paradigm is increasingly utilized as potential hosts for users' massive dataset. Since the cloud service provider (CSP) is outside the users' trusted domain, existing research suggests encrypting sensitive data before outsourcing and adopting Searchable Symmetric Encryption (SSE) to facilitate keyword-based searches over the ciphertexts. However, it remains a challenging task to design an effective SSE scheme that simultaneously supports sublinear search time, efficient update and verification, and on-demand information retrieval. To address this, we propose a Verifiable Dynamic Encryption with Ranked Search (VDERS) scheme that allows a user to perform top-K searches on a dynamic document collection and verify the correctness of the search results in a secure and efficient way. Specifically, we first provide a basic construction, VDERS0, where a ranked inverted index and a verifiable matrix are constructed to enable verifiable document insertion in top-K searches. Then, an advanced construction, VDERS*, is devised to further support document deletion with a reduced communication cost. Extensive experiments on real datasets demonstrate the efficiency and effectiveness of our VDERS scheme.

Enhanced Location Privacy Preserving Scheme in Location-Based Services

Abstract: With the increasing popularity of mobile communication devices loaded with positioning capabilities (e.g., GPS), there is growing demand for enjoying location-based services (LBSs). An important problem in LBSs is the disclosure of a user's real location while interacting with the location service provider (LSP). To address this issue, existing solutions generally introduce a trusted Anonymizer between the users and the LSP. However, the introduction of an Anonymizer actually transfers the security risks from the LSP to the Anonymizer. Once the Anonymizer is compromised, it may put the user information in jeopardy. In this paper, we propose an enhanced-location-privacy-preserving scheme for the LBS environment. Our scheme employs an entity, termed Function Generator, to distribute the spatial transformation parameters periodically, with which the users and the LSP can perform the mutual transformation between a real location and a pseudolocation. Without the transforming parameters, the Anonymizer cannot have any knowledge about a user's real location. The main merits of our scheme include the following: 1) no fully trusted entities are required, and 2) each user can obtain accurate points of interest while preserving location privacy. The efficiency and effectiveness of the proposed scheme are validated by extensive experiments. The experimental results show that the proposed scheme preserves location privacy at low computational and communication cost.

A Survey on Recent Advances in Vehicular Network Security, Trust, and Privacy

Abstract: Vehicular ad hoc networks (VANETs) are becoming the most promising research topic in intelligent transportation systems, because they provide information to deliver comfort and safety to both drivers and passengers. However, unique characteristics of VANETs make security, privacy, and trust management challenging issues in VANETs’ design. This survey article starts with the necessary background of VANETs, followed by a brief treatment of main security services, which have been well studied in other fields. We then focus on an in-depth review of anonymous authentication schemes implemented by five pseudonymity mechanisms. Because of the predictable dynamics of vehicles, anonymity is necessary but not sufficient to thwart tracking an attack that aims at the drivers’ location profiles. Thus, several location privacy protection mechanisms based on pseudonymity are elaborated to further protect the vehicles’ privacy and guarantee the quality of location-based services simultaneously. We also give a comprehensive analysis on various trust management models in VANETs. Finally, considering that current and near-future applications in VANETs are evaluated by simulation, we give a much-needed update on the latest mobility and network simulators as well as the integrated simulation platforms. In sum, this paper is carefully positioned to avoid overlap with existing surveys by filling the gaps and reporting the latest advances in VANETs while keeping it self-explained.

Data Security and Privacy-Preserving in Edge Computing Paradigm: Survey and Open Issues

Abstract: With the explosive growth of Internet of Things devices and massive data produced at the edge of the network, the traditional centralized cloud computing model has come to a bottleneck due to the bandwidth limitation and resources constraint. Therefore, edge computing, which enables storing and processing data at the edge of the network, has emerged as a promising technology in recent years. However, the unique features of edge computing, such as content perception, real-time computing, and parallel processing, has also introduced several new challenges in the field of data security and privacy-preserving, which are also the key concerns of the other prevailing computing paradigms, such as cloud computing, mobile cloud computing, and fog computing. Despites its importance, there still lacks a survey on the recent research advance of data security and privacy-preserving in the field of edge computing. In this paper, we present a comprehensive analysis of the data security and privacy threats, protection technologies, and countermeasures inherent in edge computing. Specifically, we first make an overview of edge computing, including forming factors, definition, architecture, and several essential applications. Next, a detailed analysis of data security and privacy requirements, challenges, and mechanisms in edge computing are presented. Then, the cryptography-based technologies for solving data security and privacy issues are summarized. The state-of-the-art data security and privacy solutions in edge-related paradigms are also surveyed. Finally, we propose several open research directions of data security in the field of edge computing.

MTES: An Intelligent Trust Evaluation Scheme in Sensor-Cloud-Enabled Industrial Internet of Things

Abstract: As an enabler for smart industrial Internet of Things (IoT), sensor cloud facilitates data collection, processing, analysis, storage, and sharing on demand. However, compromised or malicious sensor nodes may cause the collected data to be invalid or even endanger the normal operation of an entire IoT system. Therefore, designing an effective mechanism to ensure the trustworthiness of sensor nodes is a critical issue. However, existing cloud computing models cannot provide direct and effective management for the sensor nodes. Meanwhile, the insufficient computation and storage ability of sensor nodes makes them incapable of performing complex intelligent algorithms. To this end, mobile edge nodes with relatively strong computation and storage ability are exploited to provide intelligent trust evaluation and management for sensor nodes. In this article, a mobile edge computing-based intelligent trust evaluation scheme is proposed to comprehensively evaluate the trustworthiness of sensor nodes using probabilistic graphical model. The proposed mechanism evaluates the trustworthiness of sensor nodes from data collection and communication behavior. Moreover, the moving path for the edge nodes is scheduled to improve the probability of direct trust evaluation and decrease the moving distance. An approximation algorithm with provable performance is designed. Extensive experiments validate that our method can effectively ensure the trustworthiness of sensor nodes and decrease the energy consumption.