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Understanding human trajectory patterns is an important task in many location based social networks (LBSNs) applications, such as personalized recommendation and preference-based route planning. Most of the existing methods classify a trajectory (or its segments) based on spatio-temporal values and activities, into some predefined categories, e.g., walking or jogging. We tackle a novel trajectory classification problem: we identify and link trajectories to users who generate them in the LBSNs, a problem called Trajectory-User Linking (TUL). Solving the TUL problem is not a trivial task because: (1) the number of the classes (i.e., users) is much larger than the number of motion patterns in the common trajectory classification problems; and (2) the location based trajectory data, especially the check-ins, are often extremely sparse. To address these challenges, a Recurrent Neural Networks (RNN) based semisupervised learning model, called TULER (TUL via Embedding and RNN) is proposed, which exploits the spatio-temporal data to capture the underlying semantics of user mobility patterns. Experiments conducted on real-world datasets demonstrate that TULER achieves better accuracy than the existing methods.

A bloom filter is a simple, space-efficient, randomized data structure for concisely representing a static data set, in order to support approximate membership queries. It has great potential for distributed applications where systems need to share information about what resources they have. The space efficiency is achieved at the cost of a small probability of false positive in membership queries. However, for many applications the space savings and short locating time consistently outweigh this drawback. In this paper, we introduce dynamic bloom filters (DBF) to support concise representation and approximate membership queries of dynamic sets, and study the false positive probability and union algebra operations. We prove that DBF can control the false positive probability at a low level by adjusting the number of standard bloom filters used according to the actual size of current dynamic set. The space complexity is also acceptable if the actual size of dynamic set does not deviate too much from the predefined threshold. Furthermore, we present multidimension dynamic bloom filters (MDDBF) to support concise representation and approximate membership queries of dynamic sets in multiple attribute dimensions, and study the false positive probability and union algebra operations through mathematic analysis and experimentation. We also explore the optimization approach and three network applications of bloom filters, namely bloom joins, informed search, and global index implementation. Our simulation shows that informed search based on bloom filters can obtain higher recall and success rate of query than the blind search protocol.

Bloom filter (BF) has been widely used to support membership query, i.e., to judge whether a given element x is a member of a given set S or not. Recent years have seen a flourish design explosion of BF due to its characteristic of space-efficiency and the functionality of constant-time membership query. The existing reviews or surveys mainly focus on the applications of BF, but fall short in covering the current trends, thereby lacking intrinsic understanding of their design philosophy. To this end, this survey provides an overview of BF and its variants, with an emphasis on the optimization techniques. Basically, we survey the existing variants from two dimensions, i.e., performance and generalization. To improve the performance, dozens of variants devote themselves to reducing the false positives and implementation costs. Besides, tens of variants generalize the BF framework in more scenarios by diversifying the input sets and enriching the output functionalities. To summarize the existing efforts, we conduct an in-depth study of the existing literature on BF optimization, covering more than 60 variants. We unearth the design philosophy of these variants and elaborate how the employed optimization techniques improve BF. Furthermore, comprehensive analysis and qualitative comparison are conducted from the perspectives of BF components. Lastly, we highlight the future trends of designing BFs. This is, to the best of our knowledge, the first survey that accomplishes such goals.

Nowadays E-health cloud systems are more and more widely employed. However the security of these systems needs more consideration due to the sensitive health information of patients. So far, some protocols about secure e-health cloud systems have been proposed, but many of them use the traditional PKI infrastructure to implement cryptographic mechanisms, which is cumbersome as they require every user having and remembering its own public/private keys. Identity based encryption (IBE) is a cryptographic primitive, which uses the identity information of the user (e.g., email address) as the public key. Hence, the public key is implicitly authenticated and the certificate management is greatly simplified. On the other hand, proxy re-encryption is a cryptographic primitive aiming at transforming a ciphertext under the delegator A's into another ciphertext, which can be decrypted by the delegatee B. In this paper, we describe several identity related cryptographic techniques for securing an E-health system, which include new IBE schemes and new identity based proxy re-encryption (IBPRE) schemes. We also prove these schemes' security and give their performance analysis. Our results show that our IBPRE scheme is especially highly efficient for re-encryption, which can be used to achieve cost-effective cloud usage.

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Abstract-The topological properties of peer-to-peer overlay networks are critical factors that dominate the performance of these systems. Several non-constant and constant degree interconnection networks have been used as topologies of many peerto-peer networks. One of these has many desirable properties: the Kautz digraph. Unlike interconnection networks, peer-to-peer networks need a topology with an arbitrary size and degree, but the complete Kautz digraph does not possess these properties. In this paper, we propose MOORE: the first effective and practical peer-to-peer network based on the incomplete Kautz digraph with

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