The widespread adoption of online Large Language Models (LLMs) raises considerable privacy concerns, as prompts may inadvertently contain sensitive information exposed to LLM service providers. Limited by high computational costs, reduced response utility, and excessive system modifications, previous works based on local deployment, embedding perturbation, and homomorphic encryption are not feasible for online prompt-based LLM services. To address these issues, we introduce ProSan (Prompt Privacy Sanitizer), an end-to-end method for prompt privacy protection that generates prompts with task-irrelevant privacy removed, while preserving both utility and readability. It can also be seamlessly integrated into the online LLM service pipeline. To achieve high utility and contextual privacy, ProSan flexibly adjusts its protection targets and strength based on the importance of the words and the privacy leakage risk of the prompts. Additionally, ProSan is capable of adapting to diverse computational resource conditions, ensuring privacy protection for low-resource users. Our experiments demonstrate that ProSan effectively removes sensitive information across various tasks, including question answering, text summarization, and code generation, with minimal reduction in task performance.

With growing network service demands, rational and efficient multi-domain resource allocation is paramount. Research aims to develop intelligent Traffic Engineering (TE) algorithms that can dynamically allocate resources, adapt to changing conditions, and meet user needs. TE algorithms based on Software-Defined Networking (SDN) have proven effective for this goal by leveraging the centralized control plane and programmable data plane of SDN. This enables flexible and dynamic optimization of routing and resource allocation across multiple domains to meet traffic demands. However, domains operated by different service providers may exhibit non-cooperative behavior due to conflicts of interest and competition. Some domains may act selfishly by hiding bandwidth or exaggerating inter-domain requests to reserve more resources for themselves. This complicates TE design as algorithms can no longer assume universal cooperation in multi-domain networks. Game theory provides a framework to model competition between domains through behaviors like request forwarding, dropping and study cooperation strategies. This paper presents GameTE, a game-theoretic distributed TE algorithm for multi-domain SDN environments without trusted relationships between domains. By incorporating incentives and punishments, our algorithm suppresses selfish behaviors and promotes efficient resource utilization. Evaluation results demonstrate that GameTE is effective in curbing deception, enhancing resource sharing between domains, and improving overall network performance compared to baseline schemes.

Model publishing and deployment are essential for artificial intelligence applications. A major challenge in model publishing is efficiently distributing the models in a scalable way without violating the privacy of sensitive data. With the wide adoption of machine learning techniques, the privacy concern has also drawn much attraction. Differential privacy has become an important notion for privacy protection and is popular in private learning. However, it may bring much accuracy loss to fulfill data privacy. In addition, the private models are also hard to train in terms of convergence, which makes the existing approaches not scalable for private model publishing. This paper proposes a model publishing framework that provides a novel way to train privacy-preserving machine learning models with fast convergence and a lower privacy budget. By incorporating the concept of iterative machine teaching and the techniques in differential privacy, we have explored a way to privately select more suitable examples in the training process for achieving good accuracy with fewer iterations. Our analysis shows the privacy and convergence performance of the proposed method, and extensive experiments have been performed on real-world datasets to demonstrate its effectiveness.

Vertical federated learning (VFL) is being used more and more widely in industry. One of its most common application scenarios is a two-party setting: a participant (i.e., the host), who exclusively owns the labels but possesses insufficient number of features, wants to improve its model performance by combining features from another participant (i.e., the client) of a different business group. The best deep ML architecture suits for this scenario is considered to be Split Neural Network (SplitNN), in which each participant runs a self-defined bottom model to learn the hidden representations (i.e., the local embeddings) of its local data and then forwards them to the host, who runs a top model to aggregate both the local embeddings to produce the final predicts. In this paper, we assume the client is malicious and demonstrate that she/he could inject a stealthy backdoor into the top model during the training to misclassify any sample to a pre-selected target class with a high probability by just replacing its local embedding with a special trigger vector regardless of the host-side embedding. This task is non-trivial because existing data poison attacks for backdoor injection in traditional models usually require to modify the labels of a set of trigger-tagged samples of non-target classes, which is impossible here as the client has no rights to access or modify the labels exclusively owned by the host. Targeting this challenge, we propose a SplitNN-dedicated data poison attack which does not require to modify any labels but just replaces the local embeddings of a very small number of target-class samples with a carefully constructed trigger vector during training. The experiments on four datasets show that our attack can achieve an attack rate as high as 94%, while bringing negligible side-effects to the model accuracy. Moreover, it is stealthy enough to resist various anomaly detection methods.

Location-based services have significantly affected mobile users’ everyday life, and location privacy has become essential. Some applications (e.g., location-based recommendation, mobility analytics) do not need the raw location data, and the service providers adopt aggregation to protect users’ location traces. However, some works show that even these aggregation data may disclose users’ location privacy when additional prior knowledge is available to an adversary. We consider the location privacy problem in the presence ofLocation Uniqueness, a property by which some geographical locations can be re-identified based on the aggregated point-of-interest information. We first study whether existing protection mechanisms are adequate for defending against this type of attack. Then we present two practical attacks for inferring users’ actual locations based on the POI aggregates. A secure POI aggregate release mechanism is proposed for defending against this type of re-identification attack and achieving differential privacy at the same time. We conduct extensive experiments on real-world datasets. The results show that the existing protection mechanisms cannot provide sufficient protection against location re-identification attacks. The proposed attacks can significantly improve the inference performance, and the proposed protection mechanism achieves satisfactory performance.

Many mobile apps access users’ trajectories to provide critical services (e.g., trip tracking). Unfortunately, in such apps, malicious users may upload fake trajectories to cheat providers for illegal benefits. There are few works in the literature that delicately study trajectory forgery problems. In this paper, we first take the perspective of attackers and consider how they fabricate vivid trajectories confronting a strict provider. In particular, we use the technique of adversarial examples in deep learning to propose a trajectory forgery method, which produces fake trajectories satisfying two conditions: (1) having the motion characteristics indistinguishable from those of real ones, and (2) matching a reasonable walking, cycling, or driving route when being projected to the map. We show through experiments that they can hardly be detected by mainstream trajectory service providers, even after being equipped with machine learning-based approaches. Therefore, we further present a dedicated countermeasure by validating the reasonability of reported received signal strength indicator (RSSI) data of WiFi access points (APs) nearby every location. It can well deal with the most challenging replay scenario, which can hardly be handled by existing WiFi-based location verification methods. We conduct extensive real-world experiments in three local commercial areas covering walking, cycling, and driving scenarios. Results demonstrate the high detection accuracy of this method.

In software defined networking, theflatdesign of distributed control plane enables the management of multi-domain networks that are incapable of deploying a root controller. However, it is very difficult to avoid policy conflicts between independent local controllers due to the lack of centralized arbitration. Moreover, domains without trust may not be always cooperative and could even cheat to maximize their own interests. In this article, we first consider the cooperative scenario and address the problem of traffic engineering in a flat distributed control plane. We propose a fully distributed algorithm, calledDisTE, which can provide max-min fair bandwidth allocation for flows and maximize resource utilization.DisTEalso preserves the local topology of each domain and achieves policy consistency by multiple rounds of synchronization. We then consider the non-cooperative scenario, where selfish domains may discriminate bandwidth requests from other domains or overstate theirs owns to squeeze more bandwidths.

As the mobile era matures, it is increasingly competitive to market mobile apps, forcing companies to invest heavily on mobile user acquisition campaigns. This has unfortunately given birth to a new form of Internet fraud, which we refer to as “app distribution fraud”. This new fraud involves collusion between ISPs and fraudulent app distributors where app download is hijacked/redirected. In this article, we have the unique opportunity to cooperate with a major e-commerce company (with about 0.2 billion active users per month) to take a first peek at this problem. Through the nationwide measurement results, we find that app distribution fraud is ubiquitous yet stealthy — about 1.55 percent app downloads are hijacked/redirected, affecting more than 75 percent of the cities we tested and causing an estimated 7.46 billion U.S. dollars financial loss per year. We follow up with additional measurements on the technical mechanism of the fraud and the scope of the fraud (i.e., what other apps are also affected). Surprisingly, we find that sometimes the original app a user intends to download can be replaced with a completely different app, rendering the user's device at risks.

Location-based services have significantly affected mobile users' everyday life, and location privacy is also an essential issue in these services. In some applications (e.g., location-based recommendation, mobility analytic), the raw data is not required, and the service providers adopt aggregation to protect users' location traces. However, some works show that even these aggregation data may disclose users' location privacy when other prior knowledge is available to an adversary. We consider the location privacy problem in the presence of Location Uniqueness, which is a property that some geographical locations can be re-identified based on the aggregated point-of-interest (POI) information. We first study whether previous protection mechanisms are effective for defending against this novel type of attack. Then we present two practical attacks for inferring users' actual locations based on the POI aggregates. Furthermore, we propose a secure POI aggregate release mechanism that can defend against this type of re-identification attack and achieve differential privacy at the same time. We conduct extensive experiments on real-world datasets. The results show that the existing protection mechanisms cannot provide sufficient protection. The proposed enhanced attacks can significantly improve the inference performance, and the proposed protection mechanism achieves satisfactory performance.

Android shared storage is shared with all the applications (apps for short) and the user. It is common to see that a large amount of apps store different kinds of files on it. It is well known that apps granted the read or write permissions can freely access any files in the shared storage. As a consequence, the shared storage has been demonstrated to expose sensitive information and jeopardize users' privacy. In this paper, we systematically study a simple but overlooked threat related to the shared storage-the lack of input validation (e.g., integrity verifications) when consuming files on the shared storage. We argue that the untrusted input from the shared storage is a much ubiquitous problem. By undertaking an empirically study through a static analysis tool we develop, we find over 30 percent of the 13,746 analyzed popular apps on the market suffer from such problem. By investigating the types of files consumed, we find shockingly a large fraction of apps store and consume sensitive files, which allows us to construct end-to-end attacks. Considering the ubiquity of this class of vulnerabilities, we finally define better access control policies for external storage to eliminate them for most apps.

Link Layer Discovery Protocol (LLDP), which is widely used by the controller in Software-Defined Networking to discover the network topology, has been demonstrated to be unable to guarantee the integrity of its messages. Attackers could exploit this vulnerability to fabricate LLDP packets to declare a false link connecting two distant switches to the controller. By doing so, the controller would be misled to route flows to the false links, which leads to further DoS, eavesdropping and even hijacking attacks. This attack seems very similar to the well-known Worm-Hole Attack in wireless sensor networking (WSN). Nevertheless, in WSN, attackers are assumed to leverage an out-of-band wired channel to achieve the true packet transmission between the two cheating sensor nodes. Unfortunately, in SDN, there usually does not exist any out-of-band channels between the distant cheating switches. Flows misguided to the fake link will cause 100% packet loss, and thus be detected soon. In this article, we address this problem and propose the first True worm-hole attack in SDN, which could achieve packet transmission over the forged link without using any out-of-band channels. Instead, it introduces a relay host in the networks to build a completely in-band covert channel between the two cheating switches. Unlike the existing studies, a relay host is not required to be directly linked to them. Moreover, attackers are only assumed to poss the remote read and write privileges of the flow tables of the both cheating switches and do not have to alter any of their software or hardware. Our extensive experiments demonstrate the high feasibility of this attack. Both the increases of transmission delays and packet loss rates are within a reasonable range. We finally present and evaluate the countermeasures against the proposed attack.

Satellite communication is an important technology for the coverage of open country, and plays a vital role of link channel in remote Internet of Things (IoT). However, various kinds of IoT terminals may suffer from the limited battery capacity and computing capability. Therefore, this article proposes a new multiuser IoT system design, in which the satellite link is used to provide communication service for access points (AP), and allow terminals to download and upload data through APs. Then, the AP will exploit simultaneous wireless information and power transfer (SWIPT) and mobile edge computing (MEC) technologies to alleviate the deficiencies mentioned above. Moreover, the AP will equip with the full duplex (FD) and multi-input multi-output (MIMO) technologies to further improve the spectrum efficiency. Besides, a hybrid energy storage is assumed in the AP, i.e., the energy may come from power grid or renewable energy. Taking into account all issues above, we optimize the uplink achievable rate through jointly optimizing the CPU frequency, computation tasks, terminal transmitting power and the ratio of MEC task. In order to solve this optimization problem, we first decouple it into two sub-problems. For the first one, we can obtain the closed-form solution of CPU frequency. For the second one, we continue to decompose it into two non-convex problems, and then the iterative active-set method is used to solve these two problems. Numerical simulations demonstrate the effectiveness of the proposed design.

In recent years, more and more mobile applications adopt deep learning technologies, especially CNN-based image recognition. To protect service providers' interests, the CNN models are usually deployed on the cloud, and the users are required to upload raw images, which cause serious privacy concerns since images may contain sensitive information unrelated to the desired recognition tasks. The previous solution off-loads the shallow portions of the CNN to the clients, and thus the uploaded data becomes the extracted lower-level features rather than the raw images. Nevertheless, although service providers are prevented from obtaining the original images, it is still probably for them to perform some sensitive recognition tasks other than the desired one on the lower-lever features (even after being perturbed to satisfy Differential Privacy). Different from such solution, in this paper, we propose an independent local CNN, which is dedicated for the image perturbation on the clients. It is co-trained with the cloud CNN to learn to intelligently allocate diverse noises among pixels depending on their significance to the desired recognition service. Extensive experiments demonstrate that our mechanism can well prevent curious service providers from performing undesired recognition tasks while maintaining the high accuracy of the desired one.

The evolution of Internet of Things (IoT) devices and their adoption in new generation intelligent systems has generated a huge demand for wireless bandwidth. This bandwidth problem is further exacerbated by another characteristics of IoT applications, i.e., IoT devices are usually deployed in massive number, thus leading to an awkward scenario that many bandwidth-hungry devices are chasing after the very limited wireless bandwidth within a small geographic area. As such, cognitive radio has received much attention of the research community as an important means for addressing the bandwidth needs of IoT applications. When enabling IoT devices with cognitive functionalities including spectrum sensing, dynamic spectrum accessing, circumstantial perceiving, and self-learning, one will also need to fully study other critical issues such as standardization, privacy protection, and heterogeneous coexistence. In this article, we investigate the structural frameworks and potential applications of cognitive IoT. We further discuss the spectrum-based functionalities and heterogeneity for cognitive IoT. Security and privacy issues involved in cognitive IoT are also investigated. Finally, we present the key challenges and future direction of research on cognitive-radio-based IoT networks.

Multi-User MIMO (MU-MIMO) has attracted much attention due to its significant advantage of increasing the utilization ratio of wireless channels. However, Frequency-Division Duplex (FDD) systems are vulnerable to eavesdropping, since the explicit CSI feedback can be manipulated. In this paper, we show that Time-Division Duplex (TDD) systems are insecure as well. In particular, we show that it is possible to eavesdrop on other users' downloads by tuning training sequences. In order to defend MU-MIMO against such threats, we propose a secure CSI estimation scheme, which can provide correct estimates of CSI when adversarial users are in presence. We prove that our scheme is secure against training sequence based eavesdropping attack. We have implemented our scheme for TDD MU-MIMO systems and performed a series of experiments. Results demonstrate that our secure CSI estimation scheme is highly effective in protecting TDD MIMO networks against eavesdropping attack. Furthermore, we extend our scheme to support massive MU-MIMO networks, with a carefully redesigned uplink protocol and optimized power allocation to achieve higher spectral efficiency. To be more practical, we also take mismatch channel issue into our consideration. An enhancement scheme is proposed and we show that our scheme with enhancement is secure and correct under mismatch channel.

The increasing scale of software defined networks (SDN) raises the requirement of distributed control plane, for providing scalable, reliable and high performance network management capabilities. In particular, the flat design of distributed control plane enables the management of networks with multiple independent domains that are incapable of deploying a root controller. However, it is very difficult to avoid policy conflicts between multiple controllers in flat plane due to the lack of arbitration. In this paper, we address the problem of traffic engineering in a flat control plane, and design a distributed traffic engineering algorithm, called DisTE, which can provide max-min fair bandwidth allocation for flows and maximize the resource utilization, using a fully distributed arbitration mechanism. DisTE also preserves the local topology of each domain using the topology aggregation method, and supports consistency by multiple rounds of synchronizations. We examine four strategies for determining the synchronization timings, and find that linearly decreasing interval method provides a better trade-off between network utilization and time costs. Experiments on a 717-switches 5-domain network topology demonstrate that DisTE could drive the link utilization ratio to more than 93%, and reduce up to 95% convergence time at cost of 3% relative error on fairness, compared to the centralized approach.

By supporting asynchronous transmission and flexible subband (SB) setting, filtered orthogonal frequency division multiplexing (f-OFDM) has been identified as one of the most promising waveforms for future wireless communications, which makes the deep investigation on the f-OFDM an urgent mission. Therefore, this paper investigates the downlink interference of asynchronous f-OFDM systems under different SB configurations. We initially classify the overall interference into two categories, termed as the inner-SB and inter-SB interferences. Subsequently, the closed-form expressions for interference and its variances are derived on the basis of interference structures and are then verified via simulations. Some important influencing factors, such as the timing offset between the user of interest and the interfering user, the width of guard band, as well as the choice of SB filter, are simulated and analyzed. Our simulations and comparisons explicitly reveal the interference characteristic of the f-OFDM systems, which will benefit the design of the f-OFDM systems in the future.

Due to the loose authentication requirement between access points (APs) and clients, it is notoriously known that WLANs face long-standing threats such as rogue APs and network freeloading. Take the rogue AP problem as an example, unfortunately encryption alone does not provide authentication. APs need to be equipped with certificates that are trusted by clients ahead of time. This requires either the presence of PKI for APs or other forms of pre-established trust (e.g., distributing the certificates offline), none of which is widely used. Before any strong security solution is deployed, we still need a practical solution that can mitigate the problem. In this paper, we explore a non-cryptographic solution that is readily deployable today on end hosts (e.g., smartphones and laptops) without requiring any changes to the APs or the network infrastructure. The solution infers the Carrier Frequency Offsets (CFOs) of wireless devices from Channel State Information (CSI) as their hardware fingerprints without any special hardware requirement. CFO is attributed to the oscillator drift, which is a fundamental physical property that cannot be manipulated easily and remains fairly consistent over time but varies significantly across devices. The real experiments on 23 smartphones and 34 APs (with both identical and different brands) in different scenarios demonstrate that the detection rate could exceed 94%.

As location-based services (LBSs) on smartphones become increasingly popular, such services are causing serious privacy concerns, because many users are unwilling to see their location information leaked to service providers. Recently, in order to protect users’ location privacy, researchers have introducedgeo-indistinguishability, the first specialized privacy model for LBSs that can provide provable privacy guarantees. Intuitively, geo-indistinguishability means that through perturbation, any two locations within a given distance produce observations with similar distributions, and thus, attackers have no way to learn users’ real locations. However, even if geo-indistinguishability is achieved, there remains a significant threat to users’ location privacy: the privacy consumption increases with the number of queries for the existing geo-indistinguishable location perturbation mechanism, and therefore, there is a high risk of privacy violation when the number of queries is not small. In this paper, we enhance the privacy protection for LBSs by proposing an improved geo-indistinguishable mechanism. It can reduce the privacy costs to almost 0 when the user’s location satisfies a condition. We also present an improvement to further reduce the privacy costs when the above condition is not satisfied. Evaluations upon two public trace data sets show that the proposed mechanisms can dramatically save the privacy budget and thus support much more queries. The results also show that the proposed mechanisms are efficient, and their performance is controllable.

With market-driven secondary spectrum trading, licensed users can receive benefits in terms of monetary rewards or various transmission services, thus setting a fair pricing structure by suitably defining spectrum quality characteristics and accurately addressing participant's requirement is a key issue. In this paper, we investigate the pricing-based spectrum access by casting the problem of spectrum pricing into a Hotelling game model according to spectrum quality diversity. Particularly, we first build a pricing system model where unused spectrum from primary systems with different qualities forms a spectrum pool and can be divided into a number of uniform channels. A secondary user purchases a channel for usage according to its selection preference which is closely related to the channel quality and spectrum evaluation. The secondary user not only needs to consider the channel's quality and price, but also the interference cost on primary system. Detailed analysis on the policy preference of both primary system and secondary buyer are provided. By forming a game problem of spectrum pricing between primary and secondary users, we apply the Hotelling game model to handle the interaction between the participants. Specifically, by fixing Nash equilibrium of the game, an iterative algorithm for spectrum pricing is proposed based on the distribution characteristics of secondary user's preference. Essential analysis for the existence and uniqueness of the Nash equilibrium along with algorithm's convergence conditions are provided. Numerical results are also supplemented to show the effectiveness of the proposed algorithm in ensuring spectrum owner's profit.

In this paper, we propose a cooperative spectrum sharing protocol based on OFDM two-way relaying with joint optimal subcarrier and power allocation. Specifically, the secondary system helps the primary system achieve their target rates through OFDM two-way relaying, where the secondary system forwards the primary signal by using a fraction of subcarriers and power. In return, the secondary system can gain spectrum access by using the remaining subcarriers and power to transmit its own signal. Joint optimal subcarrier and power allocation is derived aiming to maximize secondary transmission rate with primary transmission rate constraint. Simulation results demonstrate a significant enhancement in spectrum efficiency compared with several benchmark schemes.

To achieve secure communication between legitimate users, a physical layer encryption scheme based on random rotation of the modulated symbol is proposed. By exploiting the random behavior and the reciprocity property of the wireless channel, the channel state information (CSI) shared between transmitter and legitimate receivers is used as a initial seed to generate chaotic sequence. The transmitter uses the chaotic sequence to rotate the modulated symbol to enhance communication security and to reduce eavesdroppers' ability to demodulated symbols correctly. Due to the fact that the eavesdropper does not posses any information about the legitimate channel because the channel response is unique to the location of the transmitter and receiver as well as the environment, the receiver is able to demodulate the random rotated symbols correctly while the eavesdroppers demodulate them erroneously. Simulation results show that bit-error-rate (BER) of the legal user matches theoretical results perfectly while the eavesdroppers' BER stays around 0.5, which means that the proposed scheme keeps data transmission under security.

The correlation coefficient of two consecutive Gaussian sequences is modulated by a binary message bit to achieve a secure communication system. The receiver of the proposed random communication system demodulates the received signal by estimating the correlation coefficient of the transmitted two consecutive sequences. Theoretical bit error rate (BER) expressions in frequency‐flat/‐selective fading channels with/without Doppler shift are derived. Simulation results show that the proposed system can achieve reasonably low BERs in an additive white Gaussian noise channel as well as a Rayleigh fading channel. More importantly, the proposed system shows good performance in resisting eavesdropping, since the transmitted sequence appears to be a Gaussian noise which is almost always inevitable in the process of wireless communications.

In collaborative data publishing (CDP), an m -adversary attack refers to a scenario where up to m malicious data providers collude to infer data records contributed by other providers. Existing solutions either rely on a trusted third party (TTP) or introduce expensive computation and communication overheads. In this paper, we present a practical distributed k -anonymization scheme, m - k -anonymization, designed to defend against m -adversary attacks without relying on any TTPs. We then prove its security in the semihonest adversary model and demonstrate how an extension of the scheme can also be proven secure in a stronger adversary model. We also evaluate its efficiency using a commonly used dataset.

Motion sensors, especially accelerometers, on smartphones have been discovered to be a powerful side channel for spying on users' privacy. In this paper, we reveal a new accelerometer-based side-channel attack which is particularly serious: malware on smartphones can easily exploit the accelerometers to trace metro riders stealthily. We first address the challenge to automatically filter out metro-related data from a mass of miscellaneous accelerometer readings, and then propose a basic attack which leverages an ensemble interval classifier built from supervised learning to infer the riding trajectory of the user. As the supervised learning requires the attacker to collect labeled training data for each station interval, this attack confronts the scalability problem in big cities with a huge metro network. We thus further present an improved attack using semi-supervised learning, which only requires the attacker to collect labeled data for a very small number of distinctive station intervals. We conduct real experiments on a large self-built dataset, which contains more than 120 h of data collected from six metro lines of three major cities. The results show that the inferring accuracy could reach 89% and 94% if the user takes the metro for four and six stations, respectively. We finally discuss possible countermeasures against the proposed attack.

Ridesharing services have gained tremendous popularity in recent years, benefiting the traffic and environment of cities to a large extent. However, with the demand of ridesharing services increasing sharply, serious privacy concerns (e.g., users' mobility patterns) of ridesharing have become a major barrier against its further development. In this paper, we study the privacy protection of users' location information in the scheduling of ridesharing services. Based on a state-of-the-art variant of differential privacy, joint differential privacy, we first propose a scheduling protocol for the purpose of protecting users' location privacy and minimizing vehicle miles in the system. Then, in order to obtain a practical solution, we investigate several techniques to enhance the proposed protocol from both the privacy and efficiency aspects. The privacy of the proposed scheduling protocol is rigorously proven. Furthermore, we extensively evaluate our proposal based on a real-world data set. The analysis and experimental results show that the proposed protocol can achieve joint differential privacy, satisfactory scheduling performance, and reasonable efficiency.

Due to the asynchronous and distributed nature of the data plane, consistent configuration updating across multiple switches is a challenging issue in Software-Defined Networking (SDN). The existing version-stamping-based mechanism (VSM) could guarantee per-packet consistency, but this mechanism is designed for non-adversarial settings and can be compromised easily by a malicious attacker. In this paper, we propose an efficient flow-ordered update mechanism that aims to provide per-packet consistency in adversarial settings. Our proposal does not need to stamp data packets with the configuration version, and is robust against both the packet-tampering and packet-dropping attacks. It outperforms a naive mechanism that simply patches VSM using digital signatures in three aspects: First, the switches in this mechanism only need to sign and verify a single control packet, which significantly improves the packet processing time. Second, it avoids keeping both old and new policies on switches during the update, and thus achieves better space efficiency. Third, it reduces the time delay for new policies to come into force. We evaluate our mechanism on a self-constructed SDN testbed and the results demonstrate high efficiency.

In this paper, a cooperative spectrum sharing protocol with quality-of-service (QoS) support for both of the primary and secondary systems is proposed. Specifically, the secondary system gains primary spectrum access by allocating a fraction of its power to forward the primary signal helping the primary system achieve the target rate, and meanwhile exploits the remaining power to transmit its own signal. We analyze the achievable rates for the primary and secondary systems, and determine the optimal power allocation such that the sum transmission rate of primary and secondary systems is maximized, while the QoS of both primary and secondary systems can be guaranteed. Simulation results demonstrate the efficiency of the proposed spectrum sharing protocol and its benefit to both primary and secondary systems.

Energy harvesting (EH) is a prominent method to prolong the operation time of energy-constrained wireless networks. Integrating EH into wireless communications to support simultaneous wireless information and power transfer (SWIPT) allows the spectrum to be used for both purposes without compromising the quality of service (QoS). In this paper, we propose a subcarrier allocation based SWIPT scheme in orthogonal frequency division multiplexing (OFDM) systems. Specifically, the received OFDM subcarriers are partitioned into two groups. A part of the received subcarriers are allocated to form one group which are used for information decoding (ID), and the remained subcarriers form another group, which are used for energy harvesting. Thus, no splitter is needed at the receiver. We study the optimal subcarrier allocation such that the harvested energy is maximized with the ID constraint. By using the Lagrangian method, we develop efficient algorithm to solve the optimization problem.

Scaling up the conventional multiple-input multiple- output (MIMO) by orders of magnitude, massive MIMO technique brings huge improvement in spectrum efficiency and energy efficiency. However, under time- division duplex (TDD) operation, the reciprocity calibration need to be necessarily investigated, since the mismatches of the transceiver radio frequency (RF) circuits at both sides of the link will make the whole communication channels non-reciprocal. In this paper, by utilizing the strong mutual coupling between adjacent antennas, a new calibration algorithm called adjacent mutual coupling method (AdjMC) is proposed for massive MIMO systems with zero forcing (ZF) precoding. Exploiting this method, the base station (BS) can perform the reciprocity calibration without the involvement of user equipments (UEs). Theoretical analysis and simulation results show that, comparing with the several methods in the least squares (LS) framework, the AdjMC method significantly reduces the complexity and achieves the high performance.

In this study, the parameter of a symmetric α ‐stable (S α S) noise sequence is modulated by the binary message sequence to achieve a secure communication system. The characteristic exponent ‘ α ’ of an S α S noise sequence carries the binary information. In order to recover the binary message sequence at the receiver, the authors propose a logarithmic moments estimator to estimate the characteristic exponent ‘ α ’ of the transmitted noise sequence. The optimal decision threshold and the minimum theoretical bit error ratio are derived. It is shown that the simulation results of the presented logarithmic moments estimator are consistent with the analytical results. Moreover, this estimator shows better performance and lower computational complexity than the conventional SINC estimator based on the fractional low‐order moment method.

In the problem of privacy-preserving collaborative data publishing, a central data publisher is responsible for aggregating sensitive data from multiple parties and then anonymizing it before publishing for data mining. In such scenarios, the data users may have a strong demand to measure the utility of the published data, since most anonymization techniques have side effects on data utility. Nevertheless, this task is non-trivial, because the utility measuring usually requires the aggregated raw data, which is not revealed to the data users due to privacy concerns. Furthermore, the data publishers may even cheat in the raw data, since no one, including the individual providers, knows the full data set. In this paper, we first propose a privacy-preserving utility verification mechanism based upon cryptographic technique for DiffPart-a differentially private scheme designed for set-valued data. This proposal can measure the data utility based upon the encrypted frequencies of the aggregated raw data instead of the plain values, which thus prevents privacy breach. Moreover, it is enabled to privately check the correctness of the encrypted frequencies provided by the publisher, which helps detect dishonest publishers. We also extend this mechanism to DiffGen-another differentially private publishing scheme designed for relational data. Our theoretical and experimental evaluations demonstrate the security and efficiency of the proposed mechanism.

Online behavioral advertising (OBA) has become one of the most successful advertising models on the Internet. Nevertheless, all existing OBA systems are broker-centric in the billing phase, which means it is the broker who exclusively determines advertisers' expenses and publishers' revenues. Consequently, a malicious broker may cheat in their tallying of ad clicks to overcharge advertisers or underpay publishers. Furthermore, as the broker cannot justify the bills, malicious advertisers may deny actual clicks to ask for refunds, and malicious publishers may claim non-existing clicks to demand extra revenue shares. This paper solves these problems by reversing the priority between the broker and the advertisers and publishers. Specifically, when users click on ads, it makes corresponding advertisers and publishers forward click reports of clients to the broker after checking, anonymizing and signing them. The broker then settles accounts with advertisers and publishers fully based on these reports. To guarantee the interests of the broker after the priority reversal, we further propose effective mechanisms for detecting underreporting advertisers and over reporting publishers, respectively.

Trajectory data, i.e., human mobility traces, is extremely valuable for a wide range of mobile applications. However, publishing raw trajectories without special sanitization poses serious threats to individual privacy. Recently, researchers begin to leverage differential privacy to solve this challenge. Nevertheless, existing mechanisms make an implicit assumption that the trajectories contain a lot of identical prefixes or n-grams, which is not true in many applications. This paper aims to remove this assumption and propose a differentially private publishing mechanism for more general time-series trajectories. One natural solution is to generalize the trajectories, i.e., merge the locations at the same time. However, trivial merging schemes may breach differential privacy. We, thus, propose the first differentially-private generalization algorithm for trajectories, which leverage a carefully-designed exponential mechanism to probabilistically merge nodes based on trajectory distances. Afterwards, we propose another efficient algorithm to release trajectories after generalization in a differential private manner. Our experiments with real-life trajectory data show that the proposed mechanism maintains high data utility and is scalable to large trajectory datasets.

Decoupling of control and data plane in Software Define Networks (SDN) creates significant flexibility in network management. As networks are evolving into a complex multi-domain multi-layer architecture, traffic engineering across multiple domains and layers entails challenges for the control plane, especially when each separate administrative domain does not disclose their network topology and resource information. In this paper, we present a hierarchical controller design over multidomain and multi-layer networks, by adopting a root controller at the top layer. We allow to aggregate network topology and QoS information into a hierarchical Network Information Base (NIB) for the confidentiality concern. Then, we devise a communication protocol, which enables controllers at different layers and domains to work collaboratively on bandwidth allocation by reading to the hierarchical NIB. We also present an improved traffic engineering algorithm by considering bandwidth and delay simultaneously, to maximize the network utilization while respecting max-min fairness. Experiments on a 717-switches 5-domain network topology demonstrate that our proposal could drive the link utilization ratio to more than 85%.

In a multiple-input multiple-output (MIMO) beam- forming system with finite rate feedback, the receiver sends back quantized channel information to the transmitter via a feedback channel. The overall system performance is degraded due to feedback errors. In this paper, we treat the feedback of the beamforming vector as a generalized vector quantizer (VQ), and adopt index assignment (IA) technique to cope with feedback errors. A lower bound to the average symbol error rate (SER) is derived, and an IA design criterion is proposed to minimize this bound, which is in accord with the pseudo-Gray coding principle in conventional IA design literature. Numerical results show that well-designed IA schemes improve the SER considerably.

In this paper, we propose a Doppler shift estimator based on the autocorrelation function (ACF) of the time-varying channel. In order to eliminate the effect of additive noise, we analyze the conditions for the estimator independent of the signal- to-noise ratio (SNR) and implement the conditions with a simple iterative process named as adaptive lag. The proposed method achieves good SNR-independent performance in wide range of velocities and SNRs.

Since the accuracy of channel prediction influences the performance of adaptive multiple-input-multiple-output (MIMO) systems in Rayleigh fading channels, we analyze the performance of adaptive V-BLAST systems with minimum-mean-square-error (MMSE) channel predictor in this paper. Considering the channel prediction error, we obtain the closed- form expressions for bit-error-rate (BER) and throughput of adaptive V-BLAST systems, and maximize the system transmission rate by choosing optimum block length under the BER constraint. The numerical results reveal the critical value of channel prediction error below which the systems can achieve the similar transmission rate to that of the systems with perfect channel prediction.

This paper addresses the problem of the soft decision equalization. We show the relation between the iterative soft decision interference cancellation (ISDIC) and probabilistic data association (PDA) multiuser detector (PDA-MUD). We prove that ISDIC is equivalent to PDA-MUD, and give the block-wise implementation of them. We also present a soft interference cancellation algorithm for the polynomial expansion linear detector. With its low complexity, simple implementations, and impressive performance offered by iterative soft-decision processing, it is an attractive candidate to deliver efficient reception solutions to practical wireless transmission systems. Simulation comparisons of the new method with ISDIC are presented under the frequency selective channels.

In this paper, a low complexity space-frequency iterative detection and decoding scheme is developed for multiuser space-time block-coded (STBC) block transmission systems, such as the cyclic prefix based single-carrier block transmission (CP-SCBT) system and OFDM system. Using the algebraic properties of such systems, the MMSE-based turbo detection algorithm can be implemented in the frequency domain and then the complexity of the matrix inversion can be reduced greatly. The performance of the iterative receiver for multiuser STBC block transmission systems with bit-interleaved coded modulation (BICM) is evaluated in mobile multipath frequency selective channels through computer simulations. It has been shown that the proposed receiver significantly outperforms the conventional noniterative receiver. Moreover, at high signal-to-noise ratio, the detrimental effects of multiple-access interference (MAI) and intersymbol interference (ISI) in the channel can almost be completely overcome by the turbo processing, and the performance is very close to that of BICM in a Gaussian channel.

We study the low complexity turbo detector for cyclic prefix based single-carrier block transmission (CP-SCBT) systems with multiple receive antennas. It has the following characteristics: firstly, it can be implemented by FFT/IFFT with low complexity; secondly, along with SISO decoder, turbo detection can be applied. Simulation results show that the iterative receiver provides much better performance than the conventional CP-SCBT system. It also outperforms its coded OFDM counterpart. The most attractive advantage is that it can be implemented by FFT/IFFT without significantly increasing the complexity of the system.