基于敏感位置多样性的LBS位置隐私保护方法研究

针对LBS查询服务中构造的匿名框或选取的锚点仍位于敏感区域而导致的位置隐私泄漏问题,提出了基于敏感位置多样性的锚点选取算法。该算法根据用户访问数量和访问高峰时段,对不同敏感位置进行定义和筛选,选择具有相似特征的其他敏感位置构成多样性区域,并以该区域形心作为查询锚点,提高用户在敏感位置出现的多样性。以该锚点为查询标志,提出一种均衡增量近邻兴趣点查询算法HINN,在无需用户提供真实位置坐标的条件下实现K近邻兴趣点查询,同时改进了SpaceTwist方法中存在的查询兴趣点围绕锚点分布的缺陷,提高了查询准确度。实验表明,本方法实现了用户在敏感区域停留时的位置隐私保护目标,同时具有良好的兴趣点查询质量和较低的通信开销。     

基于Voronoi图预划分的LBS位置隐私保护方法

为了解决服务器面临大量用户请求时匿名效率下降的问题,分别提出适用于静态用户和动态用户的协作匿名方法。首先基于Voronoi图划分全局区域,再由中心服务器组织本区域内用户实现协作匿名,由于服务器无需为每个用户单独构造匿名区,降低了服务端的负担;针对查询过程中用户提供真实位置信息带来位置隐私泄露的问题,提出了逆向增量近邻查询算法。用户以固定锚点代替真实位置,向位置服务器逐步获取兴趣点候选集并计算出想要的结果,避免位置隐私直接泄漏的同时获取精准查询结果。该算法同时解决了锚点与用户过近而带来的位置隐私被推断问题。实验表明本方法在有效保护用户位置隐私的同时,具有良好的工作效率。     

基于服务相似性的k-匿名位置隐私保护方法

针对当前基于位置的服务(LBS)系统存在的隐私保护度、位置服务质量和通信开销三者难于平衡的问题,提出了一种基于服务相似性的k-匿名位置隐私保护方法。在不改变现有LBS 系统架构的情况下,利用位置服务查询结果的相似性来辅助匿名服务器构造匿名区域,从而实现在确保用户隐私安全的基础上,有效提高服务质量和降低系统开销。最后,通过实验验证了该算法的有效性。     

基于网格标识匹配的位置隐私保护方法

在基于位置的服务中,基于可信第三方模型是当前位置隐私保护中的主要模型,但该模型存在一定的隐私泄露风险。该文提出一种基于网格标识匹配(GIM)的位置隐私保护方法,用户首先将查询区域划分为网格,并结合保序对称加密和K匿名技术,在匿名器形成K匿名,然后利用网格标识匹配返回查询结果给用户。在查询的过程中,匿名器并不知道用户的具体位置,加强了该模型中用户位置的隐私保护。同时中间匿名器仅进行简单的比较和匹配,有效缓解了匿名器的性能瓶颈问题。安全分析表明该方法能有效保护用户的位置隐私;并且通过实验验证该方法能有效减小匿名器的处理时间开销。     

移动互联网服务的隐私保护机制

探讨了考虑背景信息的位置和查询隐私保护方案,如基于背景信息的虚假位置k-匿名方案、同时保护位置和兴趣的隐私保护方案、基于交互的隐私保护方案,还探讨了基于用户隐私链拆分的实名认证和身份隐私保护策略;认为在避免可信第三方参与,敌手能够获取到背景信息的前提下,能够实现对用户身份、位置和查询隐私的保护,达到信任机制和隐私保护的有机结合将是未来隐私保护发展的趋势。     

移动社交网络中基于代理转发机制的轨迹隐私保护方法

K匿名技术是当前轨迹隐私保护的主流方法,但该方法也存在隐私泄露的风险。该文提出一种在移动社交网络中基于代理转发机制(BAFM)的轨迹隐私保护方法。该方法利用安全多方计算和内积安全计算进行隐私加密匹配,通过可信服务器在移动社交网络中找最匹配的用户做代理,然后由代理转发用户的请求到服务器进行查询,隐藏用户的真实轨迹与位置服务器的联系,有效保护用户的轨迹隐私。安全分析表明该方法能有效保护用户的轨迹隐私;同时,通过实验验证该方法相对K匿名更高效,能减小服务器的查询和通信开销。     

个性化搜索中一种基于位置服务的隐私保护方法

在基于位置服务的个性化搜索中,利用可信第三方服务器以及对等节点是保护用户隐私的主要方法,但在现实生活中,它们却是不完全可信的。为了解决这一问题,该文提出一种个性化搜索中基于位置服务的隐私保护方法。该方法通过转换用户的位置信息,并根据用户的查询类型生成用户模型,进而形成带有用户位置信息的查询矩阵,然后利用矩阵加密用户的查询,隐藏查询矩阵中的用户信息,最后根据安全内积计算返回相关性得分最高的前K个查询文件给用户。安全性分析表明该方法能有效地保护用户的查询隐私和位置隐私,通过分析与实验表明,该方法大幅度地缩短了索引构建时间,降低了通信开销,同时为用户提供了基于位置的个性化搜索结果,一定程度上解决了移动设备屏幕小带来的弊端。     

LBS中面向协同位置隐私保护的群组最近邻查询

在分析现有群组最近邻查询中位置隐私保护的基础上,提出LBS中一种面向位置隐私保护的群组最近邻查询方法。该方法采用分布式系统结构,克服了集中式匿名系统结构所存在通信瓶颈和攻击重点的缺陷。在此基础上根据用户群组的运动状态信息,提出使用位置随机扰动和门限秘密共享的Paillier密码系统来安全地计算用户群组的质心位置。于是将用户群组的最近邻查询转换为此质心的最近邻查询。与现有的相关工作相比,理论分析表明所提有关方案能够在有效抵御现有的距离交叉攻击和共谋攻击下,实现灵活的群组最近邻查询,同时耗费较低的网络资源。     

两层传感器网络中一种高效的加密数据条件聚合协议研究

提出了一种隐私保护的条件聚合协议,使存储节点在不知道数据真实值的情况下对满足条件的数据进行聚合,防止存储节点对敏感信息的泄漏。为了保护数据和查询条件的隐私性,提出了一种基于前缀成员确认和布鲁姆过滤器相结合的编码方法对数据和查询条件进行编码,实现存储节点在不知道数据真实值和查询条件真实值的情况下进行查询处理;为了对查询结果中的数据进行聚合而不暴露数据真实值,采用同态加密技术对数据进行加密,使数据在不解密的情况下能进行聚合运算。进一步,根据传感器采集数据的特点,提出了一种基于代码表的数据压缩表示及传输方法,有效减小了传感器节点和存储节点之间的通信开销。分析和实验结果验证了所提方案的有效性。     

F-Seeker：基于重匿名的粒度化好友搜索架构

针对社交网络中好友检索服务的隐私保护问题,本文提出一种基于重匿名技术的粒度化好友搜索架构F-Seeker．对用户发布的位置信息采用增强的k匿名策略—（k,m,e）-匿名,用以防止“好奇”的搜索服务提供方对用户隐私的推测．在处理好友搜索服务过程中,由服务提供方根据粒度化的可视策略对数据实施重匿名,实现了对用户位置信息粒度化的访问控制．此外,文中对发布数据采用Z序编码并在搜索过程中通过运用剪枝策略提高搜索效率．实验结果表明,文中提出的匿名策略在保护用户隐私的同时并没有大幅度地增加计算开销．     

Secure K-Nearest neighbor queries in two-tiered mobile wireless sensor networks

Tiered Mobile Wireless Sensor Network(TMWSN) is a new paradigm introduced by mobile edge computing. Now it has received wide attention because of its high scalability, robustness, deployment flexibility, and it has a wide range of application scenarios. In TMWSNs, the storage nodes are the key nodes of the network and are more easily captured and utilized by attackers. Once the storage nodes are captured by the attackers, the data stored on them will be exposed. Moreover, the query process and results will not be trusted any more. This paper mainly studies the secure KNN query technology in TMWSNs, and we propose a secure KNN query algorithm named the Basic Algorithm For Secure KNN Query(BAFSKQ) first, which can protect privacy and verify the integrity of query results. However, this algorithm has a large communication overhead in most cases. In order to solve this problem, we propose an improved algorithm named the Secure KNN Query Algorithm Based on MR-Tree(SEKQAM). The MR-Trees are used to find the K-nearest locations and help to generate a verification set to process the verification of query results. It can be proved that our algorithms can effectively guarantee the privacy of the data stored on the storage nodes and the integrity of the query results. Our experimental results also show that after introducing the MR-Trees in KNN queries on TMWSNs, the communication overhead has an effective reduction compared to BAFSKQ.     

Trajectory privacy protection method based on location obfuscation

In the process of continuous queries,a method of trajectory privacy protection based on location obfuscation was proposed to solve the problem that K-anonymity was difficult to guarantee user privacy in third party architectrue.Firstly,the (G-1) query obfuscation locations through the location prediction was obtained and the dummy location selection mechanism,and then sent them together with the user’s real query location to different anonymizers to form cloaking regions and sent them to the LBS server for queries,and the query results were returned to the user by different anonymizers.In this method,the user’s real query location was confused by the location obfuscation,and the attacker couldn’t deduce the user’s trajectory from a single anonymizer or the LBS server.The method can enhance the privacy of the user’s trajectory and can effectively solve the performance bottleneck in the single anonymizer structure.Security analysis shows the security of the proposed approach,and experiments show this method can reduce the number of interactions between the user and the LBS server and the overhead of the single anonymizer.     

蜂窝网络中环状搜索移动性管理策略

位置管理是移动通信领域的一个具有挑战性的问题,涉及到位置更新和位置查找操作.在现行蜂窝系统的位置管理策略(简称"基本策略")中,一旦移动台越区,就需要进行位置更新.由于移动台的越区具有局部性,基本策略会造成系统资源的极大浪费.因此,降低位置管理的费用成为移动通信领域的一个研究热点.该文给出不需要进行位置更新的环状搜索位置管理策略(简称"环状策略"),并推导出搜索位置区平均层数的一个公式,然后利用这一公式对基本策略、指针推进策略与环状策略的费用进行了对比研究,得出:在一定条件下,环状策略的费用要比基本策略及基本指针推进策略小.     

MapReduce框架下的优化高维索引与KNN查询

针对大规模高维数据近似查询效率低下的问题,利用MapReduce编程模型在大规模集群上的数据与任务的并行计算与处理优势,提出MapReduce框架下大规模高维数据索引及KNN查询方法（iPBM）,重点突破MapReduce数据块（block）的优化划分与各数据块对计算的共同贡献两大难题,利用两阶段数据划分策略并依据相关性与并行性原则将数据均匀分配到各数据块中,设计分布式的双层空间索引结构与并行KNN查询算法,检索时利用全局索引、局部索引与二维位码索引实现三层数据过滤,大幅缩小搜索范围并降低高维向量计算代价,实验表明iPBM对大规模高维数据的近似查询具有准确性、高效性和扩展性.     

Connected sensor cover: self-organization of sensor networks for efficient query execution

Spatial query execution is an essential functionality of a sensor network, where a query gathers sensor data within a specific geographic region. Redundancy within a sensor network can be exploited to reduce the communication cost incurred in execution of such queries. Any reduction in communication cost would result in an efficient use of the battery energy, which is very limited in sensors. One approach to reduce the communication cost of a query is to self-organize the network, in response to a query, into a topology that involves only a small subset of the sensors sufficient to process the query. The query is then executed using only the sensors in the constructed topology. The self-organization technique is beneficial for queries that run sufficiently long to amortize the communication cost incurred in self-organization. In this paper, we design and analyze algorithms for suchself-organization of a sensor network to reduce energy consumption. In particular, we develop the notion of a connected sensor cover and design a centralized approximation algorithm that constructs a topology involving a near-optimal connected sensor cover. We prove that the size of the constructed topology is within an O(logn) factor of the optimal size, where n is the network size. We develop a distributed self-organization version of the approximation algorithm, and propose several optimizations to reduce the communication overhead of the algorithm. We also design another distributed algorithm based on node priorities that has a further lower communication overhead, but does not provide any guarantee on the size of the connected sensor cover constructed. Finally, we evaluate the distributed algorithms using simulations and show that our approaches results in significant communication cost reductions.     

一种新的基于模糊聚类无线电地图的WLAN室内混合KNN/ANN定位方法

A novel radio-map establishment based on fuzzy clustering for hybrid K-Nearest Neighbor (KNN) and Artificial Neural Network (ANN) position algorithm in WLAN indoor environment is proposed. First of all, the Principal Component Analysis (PCA) is utilized for the purpose of simplifying input dimensions of position estimation algorithm and saving storage cost for the establishment of radio-map. Then, reference points (RPs) calibrated in the off-line phase are divided into separate clusters by Fuzzy C-means clustering (FCM), and membership degrees (MDs) for different clusters are also allocated to each RPs. However, the singular RPs cased by the multi-path effect significantly decreases the clustering performance. Therefore, a novel radio-map establishment method is presented based on the modification of signal samples recorded at singular RPs by surface fitting. In the on-line phase, the region which the mobile terminal (MT) belongs to is estimated according to the MDs firstly. Then, in estimated small dimensional regions, MT’s coordinates are calculated by KNN positioning method for efficiency purpose. However, for the regions including singular RPs, ANN method is utilized because of its great pattern matching ability. Furthermore, compared with other typical indoor positioning methods, feasibility and effectiveness of this hybrid KNN/ANN method are also verified by the experimental results in static and tracking situations.     

基于位置语义的路网位置隐私保护

移动用户在享受基于位置的服务（LBS）的同时受到位置隐私泄露的威胁,因而提供有效的位置隐私保护策略至关重要。传统的位置隐私保护方法主要采用空间匿名的方式,若攻击者获得了更多与匿名空间相关的背景知识,尤其是与位置相关的语义信息,就会严重降低匿名效果。为了防止由位置语义分析造成的敏感位置信息泄露,并根据移动用户活动范围大多限定为道路网络的特点,提出一种基于位置语义的路网位置隐私保护方法,充分考虑了用户的个性化隐私需求,并通过实验验证了方法的可行性及有效性。     

Dummy location selection algorithm based on location semantics and query probability

Aiming at the problem of privacy leakage caused by attackers possessing background knowledge in traditional location privacy protection schemes,a dummy location selection algorithm based on location semantics and query probability was proposed.Under the conditions that the locations in the dummy location set satisfied semantic difference,similar query probability,and geographically dispersed,it avoided attackers who filter dummy locations by combining background knowledge,and the accuracy of query results was guaranteed.Simulation experiments verify that the proposed algorithm can effectively protect the user’s location privacy.