In this paper, we consider the design of polar codes for point-to-point (P2P) MIMO-OFDM system with channel sparsity. We first adopt singular value decomposition (SVD) precoding to obtain a parallel symbol-wise fading channel with different effective signal-to-noise ratios (SNRs) on different subchannels due to the frequency selectivity. After a detailed evaluation on the effective SNRs, we show that the received symbols not only are corrupted by channel noise but also suffer from, effectively, channel erasure since some subchannels are in deep fade. Therefore, we propose the Reed Muller (RM)-channel degradation construction based on the statistical SNRs in achieving the balance between erasure correction and error correction abilities of polar codes via adjusting a tunable parameter. Numerical results show that a noticeable coding gain can be achieved by the proposed construction comparing with 5G polar codes.

In this study, we investigate the use of simultaneous transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) mounted on energy-efficient uncrewed aerial vehicles (UAVs) to support satellite Internet of Things (IoT) communications served by low-Earth orbit (LEO) satellites. First, we propose a STAR-RIS-equipped UAV framework termed integrated STAR-RIS and UAV (ISRU). Then, we aim to optimize energy efficiency by jointly adjusting the UAV’s flight path, STAR-RIS phase-shifts, and power allocation among IoT devices, all while maintaining equitable user fairness level. However, solving this problem presents considerable challenges due to the nonconvexity and NP-hardness properties of the objective function and constraints. To address, our work introduces a Dinkelbach-based alternating optimization (AO) procedure termed integrated trajectory, phase-shift, and power allocation (ITPP). Our simulation results show that the integration of ISRU and ITPP can achieve 67% higher sum-rates than non-ISRU schemes and save up to 40% more energy than unoptimized trajectory schemes.

Membership inference (MI) attacks pose a significant threat to user privacy in machine learning systems. While numerous attack mechanisms have been proposed in the literature, the lack of standardized evaluation parameters and metrics has led to inconsistent and even conflicting comparison results. To address this issue and facilitate a systematic analysis of these disparate findings, we introduce MIBench, a comprehensive benchmark that includes a suite of carefully designed evaluation scenarios (ESs) and evaluation metrics to provide a consistent framework for assessing the efficacy of various MI techniques. The ESs are crafted to encompass four critical factors: intra-dataset distance distribution, inter-sample distance within the target dataset, differential distance analysis, and inference withholding ratio. In total, MIBench includes ten typical evaluation metrics and incorporates 84 distinct ESs for each dataset. Using MIBench, we conducted a thorough comparative analysis of 15 state-of-the-art MI attacks across 588 ESs, seven widely adopted datasets, and seven representative model architectures. Our analysis revealed 83 instances of Conflicting Comparison Results (CCR), providing substantial evidence for the CCR Phenomenon. We identified two CCR types: Type 1 (single-factor) and Type 2 (dual-factor). The distribution of CCR instances across the four critical factors was: inter-sample distance (40.96%), differential distance (37.35%), inference withholding ratio (19.28%), and intra-dataset distance (2.41%). All MIBench codes and evaluations are available athttps://github.com/MIBench/MIBench.github.io/blob/main/README.md.

Millions of interconnected Internet of Things (IoT) sensors and devices collect tremendous amounts of data from real-world traffic scenarios. Route planning with IoT network could derive critical value for smart city and automatic vehicles. In the current route planning methods, the route weight only decays with time and distance separately, without considering the inherent spatiotemporal dependence. Besides, lacking of fine-grained traffic data interaction analysis in dynamic environment is another challenge for route planning. In this article, we propose a spatiotemporal interactive attention neural network for personalized route planning. First, we utilize an intelligent approach to route recommendation based on data collected by IoT under given spatial constraints. Next, we carry out traffic road network analysis with the spatial graph attention structure. Then, we develop a temporal self-attention mechanism to capture multilevel temporal relationship. In particular, we explore the influence of features, such as external attributes, the spatial correlation between different locations, and the temporal correlation between different time intervals. Finally, we build an aggregation network to allocate appropriate weights to measure these features for obtaining the potential best route selection. Route planning results show that the performance of our scheme is better than that of the baseline scheme, which proves that our method makes full use of attribute information and environmental changes. The IoT experimental results demonstrated that the presented system could be advantageous for tackling IoT scenarios in a cost-effective way.

The metaverse is at the forefront of the next-generation internet application, where billions of users seamlessly immerse themselves in a hybrid reality of physical-virtual worlds and switch between virtual environments thanks to reliable resource allocation and synchronization. However, the exponential growth of users and computationally intensive applications make joint optimization of multiple indicators challenging. Therefore, predicting user behavior is pivotal in assisting the optimization process. Although graph neural networks have demonstrated remarkable performance in traffic prediction, most existing schemes link nodes based on their distances and require significant computational resources, limiting their generalization and deployment in the metaverse. To solve this problem, we propose an efficient Spatio-temporal Identity Multi-graph convolutional network Framework (SIMF) for application-level traffic prediction in the metaverse. In the SIMF, we design a spatio-temporal embedding layer and multi-graph convolutional module to jointly capture spatio-temporal correlations among nodes (avatars) and reduce the dependence on topology information, which is more consistent with the real relationship between avatars in the metaverse. We conduct extensive experiments to evaluate the SIMF, which show that our proposed framework achieves superior accuracy even without graph information while maintaining low time complexity, making it suitable for traffic prediction in the metaverse.

The encrypted traffic classification based on federated learning has become one of the key concerns since it can effectively provide expansion and privacy protection for traffic dataset. However, existing classification models suffer from low robustness and slow convergence in the presence of abnormal traffic data on the client side. We note that the clients participating in the training are operated by humans in social networks, and their communication with each other generate social traffic. By introducing the traffic data into the federated learning classification model, the correlation between the respective small model parameters of the clients can be increased, which can be leveraged to quickly detect abnormal clients and improve the model performance. The effectiveness of the scheme is verified on a classical public dataset and the results show that this WS network structure converges the fastest and the degree distribution has an overall linear relationship with the convergence speed. Our scheme is still highly robust with abnormal data and the model convergence speed is significantly better than other methods. Compared with the existing method, the model of social networks based classification of federated encrypted traffic has 2.5 % higher accuracy, 6.1 % higher recall, and more than 39.3% fewer communication rounds, respectively.

With the rapid development of the Internet of Things and increasingly rigid communication requirements, the wireless traffic prediction framework is experiencing a transition from edge/cloud server deployment to edge–cloud collaborative deployment. However, it remains a significant challenge to balance prediction accuracy and overall complexity based on edge–cloud collaboration networks. In this article, we propose a multiple seasonal-trend decomposition using loess-based global–local traffic prediction (MSTL-GLTP) framework that assures prediction accuracy while maintaining low complexity. Specifically, we first decompose the cellular traffic into the multiseasonal, trend, and residual components through the MSTL algorithm. Subsequently, multiseasonal components are clustered and fed into the bidirectional long short-term memory (Bi-LSTM) model to capture global tendency. Meanwhile, we exploit a distance-assisted attention mechanism to minimize global loss. Besides, a local network module consisting of the temporal convolutional network (TCN) and Gaussian process regression (GPR) model is deployed in the edge devices to learn the dynamic regional and local traffic. The experimental results demonstrate that MSTL-GLTP outperforms the state-of-the-art baselines by capturing global–local spatiotemporal correlation and achieves accuracy and complexity equilibrium when predicting wireless traffic.

In session-based recommendation, the user's next possible click can solely be predicted based on historical interaction behavior in the ongoing session. Previously representative works mainly use sequence models and graph neural networks to model user's behaviors of the session. These works have achieved promising results, but each also has certain defects. In view of the shortcomings of the previous works, we propose a multi-encoder framework, under which the advantages of each encoder are retained. Different encoders are used to mine different session features and finally generate a more powerful session representation to improve the recommendation result. Furthermore, in order to improve the performance of recommendation, we introduce the inter-session collaboration information by designing a Inter-session Collaboration Module. Extensive experiments on two real-world datasets demonstrate the superiority of our method over state-of-the-art algorithms.

In social network analysis, link prediction is a task to predict the link possibility through the known information of the network structure. However, most current methods focus on the linear superposition of few social network attributes, which makes it difficult for relational content attributes to fully participate in the prediction. Moreover, obtaining low dimensional dense edge representation and edge weight from high-dimensional sparse social network plays a critical role in the improvement of prediction accuracy. In this paper, we propose a general framework that can predict the presence and weight of edges according to the local structure, topology and content of social networks. Firstly, we mine the representation of each node providing an exciting opportunity to advance our knowledge of feature extraction. Besides, based on the sparsity and high dimension, we use the joint embedding method to express the connection information and semantics information to learn the node representation. Furthermore, this study makes a significant contribution to research on convolutional neural network by encoding the corresponding type of node features and preserving the similarity between the original associated nodes. The prediction performance of edge presence and edge weight was experimentally investigated by large real-world datasets. The F1 index, which can measure the prediction effect of edge presence, is improved by at least 0.03. In addition, the MSE index and the PCC index of edge weight prediction are improved by at least 0.03 and 0.04 respectively. Our scheme could effectively capture the diversity of content embedding in different relational patterns.

Smart home is an indispensable part of Internet of Things(IoT) owing to the prompt development and application of smart devices. However, the data collected from smart homes usually need to be processed by a cloud server, which means there is a risk of leaking the privacy of users during the transmission. In this situation, edge computing is considered to be an ideal platform for smart home, which enable data to be processed at edge nodes. Unfortunately, because of unsecured Wi-Fi connection and smart devices, edge nodes also have the possibility to encounter malicious attacks. Hence, in this paper, we designed an intrusion detection system (IDS) to be deployed on edge nodes. We convert network traffic to images which are applied to train a convolutional neural network (CNN) to classify the categories of network traffic. Furthermore, Auxiliary Classifier Generative Adversarial Network (AC-GAN) is adopted to generate synthesized samples to expand the intrusion detection dataset. We experiment on the UNSW-NB15 dataset which contains substantial network traffic about the normal and anomalies. The proposed scheme is effective to minor categories of which precision could be improved 12%. Besides, the precision can reach 96% in binary classification about normal and anomaly.

With the rapid development of the mobile Internet and the increasing popularity of smart terminals, various mobile social applications are emerging. Medical data has become a valuable data asset and is being continuously explored and utilized, which has greatly promoted the improvement of the medical service level. However, publishing and using user data makes the user vulnerable to reasoning attacks. Due to the special nature of the medical field, medical data not only carries the health status of patients and medical process information but also involves individual sensitive information of a large number of patients. Allowing users to fully enjoy the advantages brought by social networks while ensuring security is an important issue that needs to be solved urgently in the era of big data. In this paper, we first provide an overview of social network data privacy risks and various types of attacks. Aiming at the privacy leakage of weighted social networks, we propose a privacy protection recommendation algorithm based on differential privacy. The algorithm utilizes the change of edge weight grouping, which greatly reduces the amount of calculation and satisfies the user's rapid response. It minimizes a privacy leak of user private data under data availability while supporting personalized rankings. Compared to the most advanced methods, this method protects users from reasoning attacks and reduces the distortion of ranking results caused by data confusion to ensure the accuracy of recommendations. Experiments on real-world datasets show that our framework can achieve more effective and lasting protection for user-sensitive data.

Deep learning algorithms, especially convolution neural networks, have attracted huge attention in the field of medical image analysis. A hospital could train a neural network to detect disease based on medical images possessing. However, the number of medical images would affect the results of training. If medical images of all hospitals are collected together, there's a risk of privacy leakage. In this paper, we apply collaborative deep learning to medical image analysis, which could help to improve the training effect. Besides, we also exploit differential privacy, the analytic Gaussian Mechanism, to prevent the leakage of information about medical images. We experiment on the Chest X-ray Images (Pneumonia) dataset. Results show that the analytic Gaussian Mechanism can protect the privacy of medical images effectively, while the influence on the results of training is small. The accuracy can be improved about 19\% via collaborative deep learning and can still remain about 18\% even when the analytic Gaussian Mechanism was used.

Outsourcing storage and computation to the cloud has become a common practice for businesses and individuals. As the cloud is semi-trusted or susceptible to attacks, many researches suggest that the outsourced data should be encrypted and then retrieved by using searchable symmetric encryption (SSE) schemes. Since the cloud is not fully trusted, we doubt whether it would always process queries correctly or not. Therefore, there is a need for users to verify their query results. Motivated by this, in this paper, we propose a publicly verifiable dynamic searchable symmetric encryption scheme based on the accumulation tree. We first construct an accumulation tree based on encrypted data and then outsource both of them to the cloud. Next, during the search operation, the cloud generates the corresponding proof according to the query result by mapping Boolean query operations to set operations, while keeping privacy preservation and achieving the verification requirements: freshness, authenticity, and completeness. Finally, we extend our scheme by dividing the accumulation tree into different small accumulation trees to make our scheme scalable. The security analysis and performance evaluation show that the proposed scheme is secure and practical.

Location-based social networks (LBSNs) make it possible for servers to record users' location histories, mine their life patterns, and infer individual preferences. As an important component of LBSNs, recommender systems gained popularity in recent years. Recommender systems can automatically list candidate locations for users according to their preferences, which is different from traditional search methods. However, making effective recommendations suffers from data sparsity. In order to relieve this problem and achieve high effectiveness, we take context information into consideration and present a personalized location recommender system considering both user preference and local features in this paper. To be specific, we apply Labeled-LDA in user preference learning and local features inference processes, which are denoted as UL-LDA model and CL-LDA model, respectively. Because of this, we can make recommendations even on the condition that users are in a new city and have little information about the city. We evaluate our approach with extensive experiments on a large-scale Foursquare dataset. The experimental results clearly validate the effectiveness of our approach.

In vehicular ad hoc networks (VANETs), when a vehicle receives a message, the certificate revocation list (CRL) checking process will operate before certificate and signature verification. However, large communication sources, storage space, and checking time are needed for CRLs that cause the privacy disclosure issue as well. To address these issues, in this paper, we propose an efficient anonymous batch authentication scheme (ABAH) to replace the CRL checking process by calculating the hash message authentication code (HMAC). In our scheme, we first divide the precinct into several domains, in which road-side units (RSUs) manage vehicles in a localized manner. Then, we adopt pseudonyms to achieve privacy-preserving and realize batch authentication by using an identity-based signature (IBS). Finally, we use HMAC to avoid the time-consuming CRL checking and to ensure the integrity of messages that may get loss in previous batch authentication. The security and performance analysis are carried out to demonstrate that ABAH is more efficient in terms of verification delay than the conventional authentication methods employing CRLs. Meanwhile, our solution can keep conditional privacy in VANETs.

Outsourcing storage and computation to the cloud has become a common practice for businesses and individuals. As the cloud is semi-trusted or susceptible to attacks, many researches suggest that the outsourced data should be encrypted and then retrieved by using searchable symmetric encryption (SSE) schemes. Since the cloud is not fully trusted, we doubt whether it would always process queries correctly or not. Therefore, there is a need for users to verify their query results. Motivated by this, in this paper, we propose a publicly verifiable dynamic searchable symmetric encryption scheme based on the accumulation tree. We first construct an accumulation tree based on encrypted data and then outsource both of them to the cloud. Next, during the search operation, the cloud generates the corresponding proof according to the query result by mapping Boolean query operations to set operations while keeping privacy-preservation and achieving the verification requirements: authenticity, freshness, and completeness. The security analysis and performance evaluation show that the proposed scheme is privacy-preserving and practical.

Recent years witness the rapid development of cloud computing and more and more data owners outsource their data to the cloud. To eliminate the disclosure of authorized data users' privacy in cloud services (e.g., cloud storage or cloud-assisted computing)-since the cloud providers cannot be fully trustworthy-several previous works have proposed considerable privacy-preserving schemes by exploiting searchable encryption. However, owing to its feature of untrusty, issue arises on how data users can verify whether the cloud has faithfully executed the search operations or not. Motivated by this question, in this paper, we propose a searchable and verifiable query scheme over encrypted data based on Counting Bloom Filter (CBF). Specifically, we deploy counting bloom filters to generate proofs for data users' queries in the private cloud; using the consistence between algebra operations on counting bloom filters and on data sets, we can verify the integrity of search result. The security analysis and performance evaluation show that the proposed scheme is privacy-preserving and is feasible to implement.

Proximity-based mobile social networks (PMSNs) are becoming increasingly popular due to the explosive growth of mobile devices in recent years, where a user can find a best matching friend within a nearby proximity through profile matching. However, the matching process calls for the exchange of users' personal information, which conflicts with their growing privacy concerns on revealing their profiles to strangers. Although a few methods have been proposed to achieve privacy-preserving friend discovery, most of them introduce tremendous communication overhead to the system so that they are not practical for resource-limited mobile devices. In this paper, we propose a private matching scheme based on blind signature for PMSNs, which can achieve a fine-grained matching and preferably protect users' privacy without relying on any Trusted Third Party (TTP). Moreover, our scheme can significantly reduce the communication overhead even when working as a group matching mode. Security analysis and detailed simulations show that the proposed scheme can achieve efficient privacy-preserving friend discovery.

With the pervasiveness of smartphones and the advance of wireless body sensor networks (WBSNs), mobile healthcare (m-healthcare) has attracted considerable interest recently. In m-Healthcare, users' smartphones serve as bridges connecting their WBSNs and the healthcare center (HCC), i.e., send users' personal health information (PHI) collected by WBSNs to the HCC and receive the feedback. However, users' smartphones are not always available (e.g., left at home or out of power), resulting in an unexpected interruption of medical services sometimes, which are not considered in most existing schemes for m-healthcare. In this paper, we propose a lightweight and privacy-preserving agent data transmission scheme for m-healthcare in opportunistic social networks on condition that the smartphone is not available. By using the proposed protocol, we can provide uninterrupted healthcare while keeping the user's identity and PHI private during the agent transmitting of PHI. Security and performance analysis show that the proposed scheme can realize privacy-preservation and achieve secure end-to-end communication for m-healthcare, and is suitable for resource-limited WBSNs.

Privacy protection is critical for Location-Based Services (LBSs). In most previous solutions, users query service data from the untrusted LBS server when needed, and discard the data immediately after use. However, the data can be cached and reused to answer future queries. This prevents some queries from being sent to the LBS server and thus improves privacy. Although a few previous works recognize the usefulness of caching for better privacy, they use caching in a pretty straightforward way, and do not show the quantitative relation between caching and privacy. In this paper, we propose a caching-based solution to protect location privacy in LBSs, and rigorously explore how much caching can be used to improve privacy. Specifically, we propose an entropy-based privacy metric which for the first time incorporates the effect of caching on privacy. Then we design two novel caching-aware dummy selection algorithms which enhance location privacy through maximizing both the privacy of the current query and the dummies' contribution to cache. Evaluations show that our algorithms provide much better privacy than previous caching-oblivious and caching-aware solutions.

Proximity-based mobile social networks (PMSNs) are a novel type of social networks, where mobile users can choose potential friends in vicinity by comparing the similarity degree between their private attributes and make new connections through the WiFi/Bluetooth interfaces on their mobile devices. Since users' personal attributes usually contain some sensitive information, users may have increasing privacy concerns and do not want to reveal their attributes to others in the process of friend discovery. In this paper, we first propose a two-party private matching algorithm, which achieves a fine-grained match and protects users' privacy preferably without reliance on any Trusted Third Party (TTP). Based on the two-party protocol, we then present a multi-party matching protocol, where a responder who satisfies the pre-defined threshold can match with a group of users without breaching their privacy. By the detailed analysis and simulations, we evaluate our two matching schemes in terms of security, communication overhead and computation overhead, which show a better performance than other related protocols.

Location-based services (LBSs) are attracting more and more attentions with the increasing popularity of mobile devices and online social networking. In LBSs, users can conveniently obtain their interested information by sending queries to the LBS server. However, users' queries include their identities, locations, interests, etc, which may result in the leakage of users' trajectory and other sensitive information. Although the existing obfuscation and location anonymization techniques along with a long-term pseudonym can protect users' location privacy to some extent, users' real identities and moving trajectories can still be deduced through long-term observation and side information aided inference attacks. To address these problems, we propose a dynamic pseudo-ID system, where unlinkable pseudo-IDs are used by users to completely hide their identities in the queries, in order to break the link between users' identities and their locations. Moreover, we employ certificates to ensure the verifiability and traceability of the dynamic pseudo-IDs. Security analysis and evaluation results show that the proposed scheme can enhance user' privacy and is feasible to implement into mobile devices.

Proximity-based mobile social networks (PMSNs) are becoming increasingly popular in recent years with the explosive growth of mobile devices, where a user can find a best matching friend in vicinity through profile matching. However, the matching process calls for the publication of users' personal information, which conflicts with users' growing privacy concerns about revealing their profiles to strangers. To achieve privacy-preserving friend discovery, many schemes are proposed based on traditional cryptographic methods, such as homomorphic and commutative encryption, which inevitably introduce tremendous overhead to the system and are not practical for the resource-limited mobile devices. In this paper, we propose an Efficient Weight-based Private Matching (EWPM) protocol, which provides a realistic matching approach considering both the number of common interests and the corresponding weights on them. In EWPM, we utilize the Confusion Matrix Transformation (CMT) algorithm to achieve a secure and efficient matching. Security analysis and detailed simulations show that our proposed scheme can realize privacy-preserving friend discovery with higher efficiency.

In Location-Based Services (LBSs) mobile users submit location-related queries to the untrusted LBS server to get service. However, such queries increasingly induce privacy concerns from mobile users. To address this problem, we propose FGcloak, a novel fine-grained spatial cloaking scheme for privacy-aware mobile users in LBSs. Based on a novel use of modified Hilbert Curve in a particular area, our scheme effectively guarantees k-anonymity and at the same time provides larger cloaking region. It also uses a parameter σ for users to make fine-grained control on the system overhead based on the resource constraints of mobile devices. Security analysis and empirical evaluation results verify the effectiveness and efficiency of our scheme.

Location-Based Service (LBS) has become a vital part of our daily life. While enjoying the convenience provided by LBS, users may lose privacy since the untrusted LBS server has all the information about users in LBS and it may track them in various ways or release their personal data to third parties. To address the privacy issue, we propose a Dummy-Location Selection (DLS) algorithm to achieve k-anonymity for users in LBS. Different from existing approaches, the DLS algorithm carefully selects dummy locations considering that side information may be exploited by adversaries. We first choose these dummy locations based on the entropy metric, and then propose an enhanced-DLS algorithm, to make sure that the selected dummy locations are spread as far as possible. Evaluation results show that the proposed DLS algorithm can significantly improve the privacy level in terms of entropy. The enhanced-DLS algorithm can enlarge the cloaking region while keeping similar privacy level as the DLS algorithm.

Location-Based Services (LBSs) have become one of the most popular activities and affected our daily life a lot. Mobile users can enjoy kinds of conveniences by submitting their location and interest-related queries to the LBS server. However, since these queries may expose sensitive information to the untrusted LBS server, privacy concerns arise. To address the serious privacy issues, we propose a novel collaborative scheme, EPcloak, which combines a privacy-preserving spatial cloaking algorithm and caching to protect user's privacy. Different from existing schemes, users in EPcloak cache their past service data for future use. When LBSs are needed, a user first uses a Local Searching Algorithm (LSA) to look for service data from the nearby friends of a collaborative group through Ad Hoc networks. If the requirements cannot be satisfied, the user uses a Spatial Cloaking Algorithm (SCA) to forward the query to another user at a certain distance away through a set of forwarders. That user will query the LBS server to get service data for a larger area that can cover the original user's query area, and send the data back to the original user. SCA protects both user's location privacy and query privacy. Evaluation results indicate that our proposed LSA and SCA are effective and efficient.

Location-based Services (LBSs) enable mobile users to request and obtain certain services based on their current locations, such as finding nearby gas station, looking for coffee shops, and using online GPS navigation, etc. As a major branch of LBSs, geosocial networking services, such as Foursquare, become popular due to the explosive growth of smartphone users. Geosocial networking services allow people to use their location information to find potential friends who have similar interests within close proximity and initiate communications with each other. However, most existing geosocial networking services ask for mobile users' current location information and store it on an untrusted server with less privacy concerns. To some extent, mobile users need to reveal their interests and physical location information to a service provider in order to realize the functionality of geosocial networking, which apparently deteriorates users' privacy on the aspects of their profiles and locations. In this paper, we propose a privacy-preserving friend search scheme in geosocial networks without relying on a trusted centralized server. Our scheme lets localization infrastructures, such as base stations, create encrypted searchable tables on an untrusted server and allow mobile users to search for their possible friends using their profiles without exposing their location information. Extensive trace-driven simulation results and analysis show both the efficiency and privacy preservation of our proposed scheme.

Making new social interactions with other users in vicinity is a crucial service in Proximity-based Mobile Social Networks (PMSNs), where a user can find a best matching friend directly through the Bluetooth/WiFi interfaces built in her mobile device. In existing work for such services, users have to publish their interests to do the matching. However, it conflicts with users' growing privacy concerns about revealing their interests to strangers. To tackle this problem, we propose Weighted Average Similarity (WAS) algorithm, which considers both the number of common interests and the corresponding weights on them, to protect users' privacy without reliance on any Trusted Third Party (TTP). Users set their interests into several priority levels with different weights, then WAS can provide a high level similarity value among these participants without revealing any information about their common interests. The security and computation/communication overhead of our scheme are thoroughly analyzed and evaluated via detailed simulations.

Location-Based Services (LBSs) gain increasing popularity with the development of social networks and mobile devices. The mobile users enjoy convenience by submitting their private information. Nonetheless, the users' sensitive information may be abused by an un-trusted LBS server. Privacy concerned in LBSs can be categorized into two major types: location privacy and query privacy. In this paper, we propose a novel scheme, called Encounter-Based Privacy-Preserving Scheme (EPS), which allows a user to access an LBS server under the protection of k-anonymity on both her location privacy and query privacy. Without reliance on any Trusted Third Party (TTP), EPS uses a buffer on each user's mobile device to collect the queried information of the encountered users. To achieve k-anonymity, a user needs to choose k-1 records from her buffer, with the help of our location obfuscating algorithm and querying algorithm, the user's privacy can be protected. Evaluation results show the effectiveness and efficiency of our proposed EPS.

Location-Based Services (LBSs) are becoming increasingly popular in our daily life. In some scenarios, multiple users may seek data of same interest from a LBS server simultaneously or one by one, and they may need to provide their exact locations to the un-trusted LBS server in order to enjoy such a location-based service. Unfortunately, this will breach users' location privacy and security. To address this problem, we propose a novel collaborative system, MobiCache, which combines k-anonymity with caching together to protect user's location privacy while improving the cache hit ratio. Different from the traditional k-anonymity, our Dummy Selection Algorithm (DSA) chooses dummy locations which have not been queried before to increase the cache hit ratio. We also propose an enhanced-DSA to further improve the user's privacy as well as the cache hit ratio by assigning dummy locations which can make more contributions to cache hit ratio. Evaluation results show that the proposed DSA can increase the cache hit ratio and the enhanced-DSA can further improve the cache hit ratio as well as the user's privacy.

With rapid developments of mobile devices and online social networks, users of Proximity-based Mobile Social Networks (PMSNs) can easily discover and make new social interactions with others at the cost of their growing privacy concerns. To address this problem, we propose a third party free scheme, P-match, to privately match the similarity with potential friends in vicinity. Unlike most existing work, P-match considers both the number of common interests and the corresponding priorities on each of them individually. The security and performance overhead of our scheme are then thoroughly analyzed and evaluated via detailed simulations.

Addressing security and privacy issues is a prerequisite for market-ready Vehicular Ad Hoc Networks. In this paper, an anonymous batch authentication scheme is proposed to authenticate multiple requests sent from different vehicles at the same time. The scheme achieves privacy-preserving by pseudonyms, ensures the backward privacy of the revoked vehicles by hash chain, and realize the batch authentication by using an identity-based signature (IBS). To avoid the communication overhead caused by broadcasting the Certificate Revocation List (CRL) and the privacy disclosure issue of the revocation vehicles, we revoke the illegal vehicles through calculating Hash Message Authentication Code (HMAC) by using the group key. In addition, integrity of the batch messages is ensured and efficient batch authentication is achieved. The analysis shows that our scheme has a better performance than the current batch authentication schemes on authentication delay and revocation overhead. The generated group key and pair key during the authenticated process can be used for value-added service, as the realization of HMAC doesn't require additional overhead.

Location-Based Services (LBSs) are becoming increasingly popular with rapid developments of social networks and location aware devices, such as smartphones and tablets. Users query the LBSs server and get service information about their surroundings. Unfortunately, these queries may lead to serious security and privacy concerns. It is very hard for users to access LBSs while keeping their privacy at the same time. To deal with this problem, we propose a novel scheme, called 3PLUS. It can significantly improve users' location privacy without reliance on the Trusted Third Party (TTP). Further more, it is simple to implement, and does not require changing the current structure of LBSs server. Users use a buffer to record the pseudo-locations, which come from both the history locations of herself and the encountered users. When two users encounter, by using their pseudonyms, they randomly choose a pseudo-location from their buffers and exchange with each other. Then she can find and submit k valid locations together to un-trusted LBSs server easily when the service is needed. Our evaluation results indicate us a hidden relationship between k, the buffer size S and exchanging number N.

Existing work on RFID authentication problems always make assumptions that 1) hash function can be fully used in designing RFID protocols; 2) channels between readers and the server are always secure. However, the first assumption is not suitable for EPC Class-1 Gen-2 tags, which has been challenged in many research work, while the second one cannot be adopted in mobile RFID applications where the wireless channels between readers and the server are always insecure. In this paper, we propose a new ultralightweight authentication protocol for mobile RFID systems. We only use bitwise XOR, and special constructed pseudo-random number generators (RNGs) to achieve our aims in insecure mobile RFID environment. Security analysis shows that our protocol can provide several privacy properties and avoid suffering from kinds of attacks, including tag anonymity, tag location privacy, reader privacy, forward secrecy, and mutual authentication, replay attack, desynchronization attack etc. We implement our protocol and compare authentication delays with several existing work, the results indicate us that our protocol significantly improves the efficiency.

Network coding is widely recognized as a promising approach to increase the throughput of wireless networks. In order to maximize the benefit of network coding, the consideration of potential coding opportunities is incorporated into the route selection, which is referred to as coding-aware routing. Most of existing coding-aware routing metrics are designed based on traditional routing metrics like expected transmission count (ETX) and fail to take many critical factors into account, such as traffic load, link transmission rate and interference. Therefore, the routes discovered with them are always sub-optimal. In this paper, we present a novel routing metric called Coding-aware Path Transmission Time (CPTT). CPTT considers the effect of traffic load, multirate, intra-flow and inter-flow interference as well as network coding and quantifies them in a unified manner, which can be used to accurately evaluate path performance and discover the path with high throughput. Through extensive simulations, we compare CPTT with different coding-aware routing metrics proposed in the literature and show that the paths selected with CPTT have maximum end-to-end throughput under network coding.

Online Social Networks (OSNs) are becoming immensely popular nowadays, and they change the ways people think and live. In this paper, we propose a novel reputation system which allows users to find potential connections between unfamiliar people based on the most updated friend list of each user in OSNs. To some extent, our scheme provides a way to judge people in OSNs without real interactions, but based on the existing overall attitudes on particular people. Moreover, our scheme can protect the confidentially of the potential relationships in which no one is able to acquire the detailed connections between two end nodes. Contrary to those which publish each individual's reputation online, we treat the reputation value in our system as a private issue that has been carefully guaranteed.

An Unattended Wireless Sensor Network (UWSN) collects the sensing data by using mobile sinks (MSs). It differs from the traditional multi-hop wireless sensor networks in which unbalanced traffic makes the sensors close to the base station deplete their power earlier than others. An UWSN can save the battery power and prolong the network lifetime. Unfortunately, MSs would be given too much privilege when acting as the collecting base station, which will cause security concern if compromised. Besides, UWSNs are usually deployed in unreachable and hostile environments, where sensors can be easily compromised. Thus, their security issues should be carefully addressed to deal with node compromise. In this paper, we present a novel key management scheme to secure UWSNs. We employ the Blundo symmetric polynomial mechanism to guard against the newly compromised nodes in a period while utilizing the periodic key updating based on the reverse hash chain to block the compromised nodes and revoke the compromised MSs if failing the authentication. We show that our scheme is robust against node compromised attacks and carry out comparison analysis on the intrusion-tolerance ratio, communication and computing overhead.

Privacy concern is arguably the major barrier that hinders the deployment of electronic health record (EHR) systems which are considered more efficient, less error-prone, and of higher availability compared to traditional paper record systems. Patients are unwilling to accept the EHR system unless their protected health information (PHI) containing highly confidential data is guaranteed proper use and disclosure, which cannot be easily achieved without patients' control over their own PHI. However, cautions must be taken to handle emergencies in which the patient may be physically incompetent to retrieve the controlled PHI for emergency treatment. In this paper, we propose a secure EHR system, HCPP (Healthcaresystem for Patient Privacy), based on cryptographic constructions and existing wireless network infrastructures, to provide privacy protection to patients under any circumstances while enabling timelyPHI retrieval for life-saving treatment in emergency situations. Furthermore, our HCPP system restricts PHI access to authorized (not arbitrary) physicians, who can be traced and held accountable if the accessed PHI is found improperly disclosed. Last but not least, HCPP leverages wireless network access to support efficient and private storage/retrieval of PHI, which underlies a secure and feasible EHR system.

For a multi-hop wireless network (MWN) consisting of mobile nodes controlled by independent self-interested users, incentive mechanism is essential for motivating mobile nodes to cooperate and forward packets for each other. Existing solutions such as barter based, virtual-currency based and reputation based schemes are either less effective or incur high implementation costs, and therefore do not fit well with the unique requirements of MWNs. In this paper, we propose a novel and promising incentive paradigm, Controlled Coded packets as virtual Commodity Currency (C4), to induce cooperative behaviors in MWNs. In our C4, through introducing several techniques from network coding, coded information packets are utilized as a new kind of virtual currency to facilitate packet/service exchanges among self-interested nodes in a MWN. Since the virtual currency implemented in this way also carries useful data information, it is the counterpart of the so-called commodity currency in the physical world, and the overhead brought by C4 is extremely small compared to traditional schemes. We theoretically show that C4 is perfectly efficient to support MWNs with broadcast and multicast traffics. For pure unicast communications, by adjusting the grouping parameter, our C4 provides a systematic way to smoothly trade incentive effectiveness for implementation cost, and traditional barter based and virtual-currency based schemes are just two extreme cases of C4. We also show that when our C4 is combined with the social network formed by mobile users in the MWN, the implementation costs can be further reduced without sacrificing incentive effectiveness.

Natural disasters and terrorism threaten our nation's safety and security, rendering post-disaster rescue mission critical. It is of paramount importance to carry out rescue work relying on secure and dependable networking. In this paper, we propose RescueMe, location-based vehicular ad hoc networks (VANETs), to aid in secure and dependable rescue planning for the efficient allocation of rescue resources. RescueMe leverages the location information stored during normal network operations to facilitate post-disaster rescue planning, while guaranteeing that the sensitive user location information is not exploited to trace a user's whereabouts when disasters are absent, even if the most powerful collusion attack is allowed. We provide a novel construction for the location update message, and propose several enhancements, to achieve the functional and security goals of RescueMe.

E-healthcare is becoming a vital part of our living environment and exhibits advantages over paper-based legacy systems. Wireless body sensor networks are indispensable in one application of e-healthcare, the remote monitoring or remote care services. However, privacy is the foremost concern of the patients and the biggest impediment of the deployment of e-healthcare systems. In addressing privacy issues, conflicts from the functional requirements must be taken into account. One such requirement is the efficient and effective response to medical emergencies. In this paper, we propose to solve these conflicting goals based on suitable cryptographic schemes. In addition, security enhancements are proposed which satisfy other fundamental security goals besides the privacy requirements.

Online social networks (OSNs) are attractive applications which enable a group of users to share data and stay connected. Facebook, Myspace, and Twitter are among the most popular applications of OSNs where personal information is shared among group contacts. Due to the private nature of the shared information, data privacy is an indispensable security requirement in OSN applications. In this paper, we propose a privacy-preserving scheme for data sharing in OSNs, with efficient revocation for deterring a contact's access right to the private data once the contact is removed from the social group. In addition, the proposed scheme offers advanced features such as efficient search over encrypted data files and dynamic changes to group membership. With slight modification, we extend the application of the proposed scheme to anonymous online social networks of different security and functional requirements. The proposed scheme is demonstrated to be secure, effective, and efficient.

Recently, trust-based routing has received much attention as an effective way to improve security of wireless ad hoc networks (WANETs). Although various trust metrics have been designed and incorporated into the routing metrics, as far as we know, none of the existing works have used mathematical tools such as routing algebra to analyze the compatibility of trust related routing metrics and routing protocols in WANETs. In this paper, we first identify unique features of trust metrics compared with QoS-based routing metrics. Then, we provide a systematic analysis of the relationship between trust metrics and trust-based routing protocols by identifying the basic algebraic properties that a trust metric must have in order to work correctly and optimally with different generalized distance-vector or link-state routing protocols in WANETs. Moreover, we extend our framework to model the interactions between different trust-based routing protocols. Finally, our results are applied to check the compatibility of the trust metrics proposed in previous literature and the popular routing protocols used in WANETs.

Conventional block-based multicast authentication schemes overlook the heterogeneity of receivers by letting the sender choose the block size, divide a multicast stream into blocks, associate each block with a signature, and spread the effect of the signature across all the packets in the block through hash graphs or coding algorithms. The correlation among packets makes them vulnerable to packet loss, which is inherent in the Internet and wireless networks. Moreover, the lack of Denial of Service (DoS) resilience renders most of them vulnerable to packet injection in hostile environments. In this paper, we propose a novel multicast authentication protocol, namely MABS, including two schemes. The basic scheme (MABS-B) eliminates the correlation among packets and thus provides the perfect resilience to packet loss, and it is also efficient in terms of latency, computation, and communication overhead due to an efficient cryptographic primitive called batch signature, which supports the authentication of any number of packets simultaneously. We also present an enhanced scheme MABS-E, which combines the basic scheme with a packet filtering mechanism to alleviate the DoS impact while preserving the perfect resilience to packet loss.

Rate adaptation algorithms play a crucial role in IEEE 802.11 WLANs. While the network performance depends greatly on the rate adaptation algorithms, the detailed implementation is left to vendors. Due to its simplicity and practicality, threshold-based rate adaptation algorithms are widely adopted in commercial IEEE 802.11 devices. Taking the popular ARF algorithm for example, the data rate is increased when ten consecutive transmissions are successful and a date rate downshift is triggered by two consecutive failed transmissions. Although widely deployed, the optimal selection of the up/down thresholds for the rate adaptation algorithms remains an open problem. In this paper, we first investigate the threshold-based rate adaptation algorithm via a reverse engineering approach where the implicit objective function is revealed. Next, we propose a threshold optimization algorithm which can dynamically adjust the up/down thresholds and converge to the stochastic optimum solution in arbitrary stationary random channel environment. The performance enhancement by tuning the thresholds optimally is validated by simulations.

This paper proposes C&M, a novel network coding scheme to deal with both inter-flow and intra-flow traffic in wireless networks. Prior work on wireless network coding design belongs to either inter-flow network coding or intra-flow network coding. C&M attempts to combine advantages of both network coding approaches to develop a more efficient network coding scheme. Based on COPE, C&M allows each node to make use of intra-flow network coding such as MORE to improve the transmission reliability in a lossy environment, consequently obtaining higher throughput. Moreover, we propose the multiple-path transmitting scheme to increase the throughput of wireless networks with low link delivery probability further. Finally, we provide our view on how to integrate network coding into network stack and discuss some potential benefits of C&M.

Rate adaptation algorithms play a crucial role in IEEE 802.11 WLANs. While the network performance depends greatly on the rate adaptation algorithms, the detailed implementation is left to vendors. Due to its simplicity and practicality, the generic rate adaptation algorithm based on up/down thresholds is widely adopted in commercial IEEE 802.11 devices. Taking the popular ARF algorithm for example, the data rate is increased when ten consecutive transmissions are successful and a date rate downshift is triggered by two consecutive failed transmissions. Although widely deployed, disclosing the implicit objective function that the rate adaptation algorithm is dynamically maximizing, remains as an open problem in the literature. In this paper, we investigate the thresholds-based rate adaptation algorithm via a reverse engineering perspective where the implicit objective function is revealed. We consider this reverse engineering study of the thresholds-based rate adaptation algorithm as an important first step towards a comprehensive understanding on the rate adaptation mechanism designs and the complex interactions among multiple IEEE 802.11 stations.

This paper characterizes the throughput-delay tradeoffs in mobile ad hoc networks (MANETs) with network coding, and compares results in the situation where only replication and forwarding are allowed in each node. The schemes/protocols achieving those tradeoffs in an effective and decentralized way are proposed and the optimality of those tradeoffs is established. The scenarios in which network coding can provide significant improvement on network performance are identified under different node mobility patterns (fast and slow mobility). The insights on when and how information mixing is beneficial for MANETs with multiple unicast and multicast sessions are provided. As far as we know, this is the first work characterizing scaling laws of throughput and delay of MANETs with network coding.

This paper investigates the problem of how much benefit network coding can contribute to the network performance in terms of throughput, delay, and storage requirements for mobile ad hoc networks (MANETs), compared to when only replication, storage and forwarding are allowed in relay nodes. We characterize the throughput-delay-storage tradeoffs under different node mobility patterns, i.e., i.i.d. and random walk mobility, with and without network coding. Our results show that when random linear coding instead of replication is used in MANETs, an order improvement on the scaling laws of MANETs can be achieved. Note that previous work showed that network coding could only provide constant improvement on the throughput of static wireless networks. Our work thus differentiates MANETs from static wireless networks by the role network coding plays.