无线传感器网络隐私保护关键技术研究
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摘要
无线传感器网络一般由成本低廉、功能丰富的传感器节点自组织构成,通常简称无线传感网。近年来,无线传感器网络在军事、医疗、环保、智能家庭等多个领域均表现出非常可观的应用前景,并因此受到学术界和工业界的高度重视。随着无线传感器网络相关技术的发展与成熟,日益严重的隐私问题已经逐步成为制约无线传感器网络部署应用的瓶颈问题。由于无线传感器网络特有的资源严重受限、应用相关等特点,使得其隐私问题呈现出鲜明而独特的特征,导致现有的隐私保护机制和多数算法无法有效适用,从而对隐私保护机制的设计提出了紧迫需求与重要挑战。
本文以无线传感网中的隐私保护问题为主要研究对象,紧密围绕无线传感网中的若干关键技术问题开展了深入研究。在对无线传感器网络隐私威胁及保护需求进行深入分析的基础上,分别针对位置隐私推断攻击防护机制、车载移动传感网身份隐私保护协议及多媒体传感网数据隐私保护的任务分配与调度方法等问题开展深入研究,建立了对应的数学模型,并提出了相应的方案、算法与协议,从而取得了一系列研究成果：
根据保护目标对象的不同,无线传感网的隐私问题主要包含数据隐私、位置隐私与身份隐私等三类。本文对无线传感网中的隐私保护问题进行了深入研究,对现有的方案与机制进行了细致的分类总结、分析与述评,其研究成果对国内开展这一热点方向的研究具有一定的参考价值。
位置隐私是目标监测型传感器网络中所面临的关键挑战之一。目前,这一领域的现有研究大多仅针对目标对象的当前位置实施保护,而忽略了连续多个位置之间的关联关系。事实上,现有的成熟位置推断技术可以利用目标物的连续历史位置信息推断其当前位置,这为发起推断攻击提供了可能,并由此引发了严重的安全与隐私威胁。本文提出了连续位置推断隐私攻击及保护问题,对其进行了深入与分析,并构建了相应的K-连续隐私保护模型。在此模型基础上,发现了隐私保护程度与数据效用之间的权衡关系,并基于此定义了最大可公开序列问题,指出其是NP-完全的,并随之开展了相关研究。随后,我们针对在线条件的最大可公开序列问题进行了深入分析,指出在p-关联条件下该问题可以采用动态规划算法获取最优解,而对于一般通用情形,则提出了两种不同启发式策略下的算法。接着,依据分布式条件下的隐私保护协议需求,设计了P-preserving协议。实验与分析结果验证了上述算法与协议的有效性。
身份隐私保护机制是新兴的车载移动传感网中亟待解决的关键性问题。然而,现有车载传感网隐私保护机制很难提供一般城市环境支持,从而限制了其应用范围。着重分析了在这一应用场景下车载移动传感网的主要特征,提出了一种基于短期证书的条件隐私保护协议SCPP。SCPP不仅可以成功应用于一般城市环境,表现出普适性强、易于部署等优势,能有效地满足隐私保护和身份作废的双重需求；同时,与现有隐私保护协议的深入比较与分析表明,在计算与通信开销等方面均优于现有方案。
随着多媒体传感网的飞速发展,其数据隐私保护机制对底层任务分配与调度技术提出了重要挑战。本文主要针对面向多媒体传感网数据隐私保护的任务分配与调度的实时低耗需求,指出在多跳环境下任务分配与调度具有拓扑敏感的特性,进而运用多目标约束建模技术,提出拓扑敏感的任务分配与调度问题TATAS,并证明该问题是NP完全的。随后提出了一个三阶段的启发式解决方案TATAS-3H。实验结果表明：与传统方法相比,新方案能有效减少能量消耗,同时满足数据隐私保护任务的实时性要求。
Wireless sensor networks, commonly abbreviated as WSNs, are usually consisted of numerous self-organized low-cost and feature-rich sensor nodes. In the recent few years, WSNs have tremendous promising alternative applications and can be widely deployed in many application areas, such as military battlefield, health cares, environmental protection, smart home, and so forth. Therefore, WSNs have drawn board attention from both industry and academia. With the rapid development and proliferation of WSNs, the increasing serious privacy concerns have gradually restricted the deployment and application of WSN. However, compared with traditional networks, WSNs are resource-constrained and application specific, which determines that their privacy problems were significantly distinguishable and unique, leading it more difficult to effectively apply existing privacy protection mechanisms and algorithms to address related problems. Consequently, it brings emergent requirements and great challenges for designing privacy protection solutions within WSNs.
In this dissertation, we focus on the privacy protection concerns in WSNs, and carried out our in-depth study aiming at addressing several key technical issues. We make our great effort to analyze the privacy threat and its corresponding protection requirements in WSNs, and then further conducted location privacy preserving inference control mechanisms towards successive source locations, designed conditional identity privacy preserving protocols for vehicular sensor networks, and proposed task allocation and scheduling approaches for data privacy protection in multimedia sensor networks. Towards these issues, based on well-established mathematical models, we proposed corresponding solutions, algorithms and protocols. Our contributions can be summarized as follows:
According to different protection objects, the privacy problem in WSNs can be classified into three categories:data privacy, location privacy and identity privacy. In this thesis, the privacy protection concerns in WSNs are analyzed in a comprehensive way, and followed by classification, analysis and reviews on typical schemes and mechanisms. We hope that our work may be helpful for domestic researchers on this hot topic.
Location Privacy is one of the major challenges in surveillance sensor networks. Currently, most research efforts focus on protecting current location, and ignore the internal relationship among the successive locations. To date, given a sequence of past observations, abundant techniques are available to infer current or future locations of an object, which may lead possible infer attacks and bring in serious privacy and security concerns. In this thesis, we for the first time proposed the successive location privacy infer attack and protection problems, and built the K-successive privacy model based on in-depth analysis of these problems. In this model, we observed that there is an intrinsic trade-off between the number of data to be published to the interested parties and the privacy preservation of the object. We then formulated the maximum publishable sequence(MPS) problem, and show it is NP-complete. Thereafter, we analyzed the online MPS problems, and developed optimal solutions to p-relation cases through dynamic programming and two heuristics for the general case. We then designed the P-preserving protocol for distributed privacy aware data collections. Extensive simulations and analysis demonstrate the effectiveness of our proposed algorithms and protocols.
Identity privacy concerns are very critical and fundamental aspects in emerging vehicular sensor networks. So far, existing privacy preservation schemes are unable to be deployed in common urban scenarios, and therefore their application becomes severely constrained. By analyzing the inherent requirements for deploying privacy preservation schemes in common urban scenarios, we propose SCPP, a novel short-time certificate privacy preservation protocol. SCPP is not only well suitable and easy-to-deploy in common urban scenarios, but also satisfy the requirements of conditional privacy preservation and identity revocation as well. Extensive analysis and comparison results show that, compared with the current best available privacy preserving protocol, our SCPP protocol performs better in terms of both computational and communication cost.
With the rapid development of multimedia sensor networks, its data privacy protection problems bring great challenges to task allocation and scheduling. To this end, by analyzing its real-time and low-power requirements, the task allocation and scheduling problem for data privacy protection in multi-hop multimedia sensor networks was found to be topology aware. By investigating multi-objective optimization and constraint modeling techniques, we formally model TATAS, the Topology-Aware Task Allocation and Scheduling problem, and prove it is NP-complete. Then, an efficient three-phase heuristic solution, named TATAS-3H, was proposed to solve the TATAS problem. Experimental results show that, as compared with traditional approaches, our technique can achieve significant energy saving and effectively meet the real-time requirements of data privacy protection as well.
本文以无线传感网中的隐私保护问题为主要研究对象,紧密围绕无线传感网中的若干关键技术问题开展了深入研究。在对无线传感器网络隐私威胁及保护需求进行深入分析的基础上,分别针对位置隐私推断攻击防护机制、车载移动传感网身份隐私保护协议及多媒体传感网数据隐私保护的任务分配与调度方法等问题开展深入研究,建立了对应的数学模型,并提出了相应的方案、算法与协议,从而取得了一系列研究成果：
根据保护目标对象的不同,无线传感网的隐私问题主要包含数据隐私、位置隐私与身份隐私等三类。本文对无线传感网中的隐私保护问题进行了深入研究,对现有的方案与机制进行了细致的分类总结、分析与述评,其研究成果对国内开展这一热点方向的研究具有一定的参考价值。
位置隐私是目标监测型传感器网络中所面临的关键挑战之一。目前,这一领域的现有研究大多仅针对目标对象的当前位置实施保护,而忽略了连续多个位置之间的关联关系。事实上,现有的成熟位置推断技术可以利用目标物的连续历史位置信息推断其当前位置,这为发起推断攻击提供了可能,并由此引发了严重的安全与隐私威胁。本文提出了连续位置推断隐私攻击及保护问题,对其进行了深入与分析,并构建了相应的K-连续隐私保护模型。在此模型基础上,发现了隐私保护程度与数据效用之间的权衡关系,并基于此定义了最大可公开序列问题,指出其是NP-完全的,并随之开展了相关研究。随后,我们针对在线条件的最大可公开序列问题进行了深入分析,指出在p-关联条件下该问题可以采用动态规划算法获取最优解,而对于一般通用情形,则提出了两种不同启发式策略下的算法。接着,依据分布式条件下的隐私保护协议需求,设计了P-preserving协议。实验与分析结果验证了上述算法与协议的有效性。
身份隐私保护机制是新兴的车载移动传感网中亟待解决的关键性问题。然而,现有车载传感网隐私保护机制很难提供一般城市环境支持,从而限制了其应用范围。着重分析了在这一应用场景下车载移动传感网的主要特征,提出了一种基于短期证书的条件隐私保护协议SCPP。SCPP不仅可以成功应用于一般城市环境,表现出普适性强、易于部署等优势,能有效地满足隐私保护和身份作废的双重需求；同时,与现有隐私保护协议的深入比较与分析表明,在计算与通信开销等方面均优于现有方案。
随着多媒体传感网的飞速发展,其数据隐私保护机制对底层任务分配与调度技术提出了重要挑战。本文主要针对面向多媒体传感网数据隐私保护的任务分配与调度的实时低耗需求,指出在多跳环境下任务分配与调度具有拓扑敏感的特性,进而运用多目标约束建模技术,提出拓扑敏感的任务分配与调度问题TATAS,并证明该问题是NP完全的。随后提出了一个三阶段的启发式解决方案TATAS-3H。实验结果表明：与传统方法相比,新方案能有效减少能量消耗,同时满足数据隐私保护任务的实时性要求。
Wireless sensor networks, commonly abbreviated as WSNs, are usually consisted of numerous self-organized low-cost and feature-rich sensor nodes. In the recent few years, WSNs have tremendous promising alternative applications and can be widely deployed in many application areas, such as military battlefield, health cares, environmental protection, smart home, and so forth. Therefore, WSNs have drawn board attention from both industry and academia. With the rapid development and proliferation of WSNs, the increasing serious privacy concerns have gradually restricted the deployment and application of WSN. However, compared with traditional networks, WSNs are resource-constrained and application specific, which determines that their privacy problems were significantly distinguishable and unique, leading it more difficult to effectively apply existing privacy protection mechanisms and algorithms to address related problems. Consequently, it brings emergent requirements and great challenges for designing privacy protection solutions within WSNs.
In this dissertation, we focus on the privacy protection concerns in WSNs, and carried out our in-depth study aiming at addressing several key technical issues. We make our great effort to analyze the privacy threat and its corresponding protection requirements in WSNs, and then further conducted location privacy preserving inference control mechanisms towards successive source locations, designed conditional identity privacy preserving protocols for vehicular sensor networks, and proposed task allocation and scheduling approaches for data privacy protection in multimedia sensor networks. Towards these issues, based on well-established mathematical models, we proposed corresponding solutions, algorithms and protocols. Our contributions can be summarized as follows:
According to different protection objects, the privacy problem in WSNs can be classified into three categories:data privacy, location privacy and identity privacy. In this thesis, the privacy protection concerns in WSNs are analyzed in a comprehensive way, and followed by classification, analysis and reviews on typical schemes and mechanisms. We hope that our work may be helpful for domestic researchers on this hot topic.
Location Privacy is one of the major challenges in surveillance sensor networks. Currently, most research efforts focus on protecting current location, and ignore the internal relationship among the successive locations. To date, given a sequence of past observations, abundant techniques are available to infer current or future locations of an object, which may lead possible infer attacks and bring in serious privacy and security concerns. In this thesis, we for the first time proposed the successive location privacy infer attack and protection problems, and built the K-successive privacy model based on in-depth analysis of these problems. In this model, we observed that there is an intrinsic trade-off between the number of data to be published to the interested parties and the privacy preservation of the object. We then formulated the maximum publishable sequence(MPS) problem, and show it is NP-complete. Thereafter, we analyzed the online MPS problems, and developed optimal solutions to p-relation cases through dynamic programming and two heuristics for the general case. We then designed the P-preserving protocol for distributed privacy aware data collections. Extensive simulations and analysis demonstrate the effectiveness of our proposed algorithms and protocols.
Identity privacy concerns are very critical and fundamental aspects in emerging vehicular sensor networks. So far, existing privacy preservation schemes are unable to be deployed in common urban scenarios, and therefore their application becomes severely constrained. By analyzing the inherent requirements for deploying privacy preservation schemes in common urban scenarios, we propose SCPP, a novel short-time certificate privacy preservation protocol. SCPP is not only well suitable and easy-to-deploy in common urban scenarios, but also satisfy the requirements of conditional privacy preservation and identity revocation as well. Extensive analysis and comparison results show that, compared with the current best available privacy preserving protocol, our SCPP protocol performs better in terms of both computational and communication cost.
With the rapid development of multimedia sensor networks, its data privacy protection problems bring great challenges to task allocation and scheduling. To this end, by analyzing its real-time and low-power requirements, the task allocation and scheduling problem for data privacy protection in multi-hop multimedia sensor networks was found to be topology aware. By investigating multi-objective optimization and constraint modeling techniques, we formally model TATAS, the Topology-Aware Task Allocation and Scheduling problem, and prove it is NP-complete. Then, an efficient three-phase heuristic solution, named TATAS-3H, was proposed to solve the TATAS problem. Experimental results show that, as compared with traditional approaches, our technique can achieve significant energy saving and effectively meet the real-time requirements of data privacy protection as well.
引文
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