δ-Calculus:A New Approach to Quantifying Location Privacy

With the rapid development of mobile wireless Internet and high-precision localization devices,location-based services(LBS)bring more convenience for people over recent years.In LBS,if the original location data are directly provided,serious privacy problems raise.As a response to these problems,a large number of location-privacy protection mechanisms(LPPMs)(including formal LPPMs,FLPPMs,etc.)and their evaluation metrics have been proposed to prevent personal location information from being leakage and quantify privacy leakage.However,existing schemes independently consider FLPPMs and evaluation metrics,without synergizing them into a unifying framework.In this paper,a unified model is proposed to synergize FLPPMs and evaluation metrics.In detail,the probabilistic process calculus(calledδ-calculus)is proposed to characterize obfuscation schemes(which is a LPPM)and integrateα-entropy toδ-calculus to evaluate its privacy leakage.Further,we use two calculus moving and probabilistic choice to model nodes’mobility and compute its probability distribution of nodes’locations,and a renaming function to model privacy leakage.By formally defining the attacker’s ability and extending relative entropy,an evaluation algorithm is proposed to quantify the leakage of location privacy.Finally,a series of examples are designed to demonstrate the efficiency of our proposed approach.

A game-theoretic approach for federated learning:A trade-off among privacy,accuracy and energy

Benefiting from the development of Federated Learning(FL)and distributed communication systems,large-scale intelligent applications become possible.Distributed devices not only provide adequate training data,but also cause privacy leakage and energy consumption.How to optimize the energy consumption in distributed communication systems,while ensuring the privacy of users and model accuracy,has become an urgent challenge.In this paper,we define the FL as a 3-layer architecture including users,agents and server.In order to find a balance among model training accuracy,privacy-preserving effect,and energy consumption,we design the training process of FL as game models.We use an extensive game tree to analyze the key elements that influence the players’decisions in the single game,and then find the incentive mechanism that meet the social norms through the repeated game.The experimental results show that the Nash equilibrium we obtained satisfies the laws of reality,and the proposed incentive mechanism can also promote users to submit high-quality data in FL.Following the multiple rounds of play,the incentive mechanism can help all players find the optimal strategies for energy,privacy,and accuracy of FL in distributed communication systems.

RFID Based Non-Preemptive Random Sleep Scheduling in WSN

In Wireless Sensor Network(WSN),because battery and energy supply are constraints,sleep scheduling is always needed to save energy while maintaining connectivity for packet delivery.Traditional schemes have to ensure high duty cycling to ensure enough percentage of active nodes and then derogate the energy efficiency.This paper proposes an RFID based non-preemptive random sleep scheduling scheme with stable low duty cycle.It employs delay tolerant network routing protocol to tackle the frequent disconnections.A low-power RFID based non-preemptive wakeup signal is used to confirm the availability of next-hop before sending packet.It eliminates energy consumption of repeated retransmission of the delayed packets.Moreover,the received wakeup signal is postponed to take effect until the sleep period is finished,and the waken node then responds to the sending node to start the packet delivery.The scheme can keep stable duty cycle and then ensure energy saving effect compared with other sleeping scheduling methods.

A blockchain-based audit approach for encrypted data in federated learning

The development of data-driven artificial intelligence technology has given birth to a variety of big data applications.Data has become an essential factor to improve these applications.Federated learning,a privacy-preserving machine learning method,is proposed to leverage data from different data owners.It is typically used in conjunction with cryptographic methods,in which data owners train the global model by sharing encrypted model updates.However,data encryption makes it difficult to identify the quality of these model updates.Malicious data owners may launch attacks such as data poisoning and free-riding.To defend against such attacks,it is necessary to find an approach to audit encrypted model updates.In this paper,we propose a blockchain-based audit approach for encrypted gradients.It uses a behavior chain to record the encrypted gradients from data owners,and an audit chain to evaluate the gradients’quality.Specifically,we propose a privacy-preserving homomorphic noise mechanism in which the noise of each gradient sums to zero after aggregation,ensuring the availability of aggregated gradient.In addition,we design a joint audit algorithm that can locate malicious data owners without decrypting individual gradients.Through security analysis and experimental evaluation,we demonstrate that our approach can defend against malicious gradient attacks in federated learning.

SWRR:The Link Scheduling Algorithm Based on Weighted Round-Robin

With the rapid development of the Internet,people pay more and more attention to the protection of privacy.The second-generation onion routing system Tor is the most commonly used among anonymous communication systems,which can be used to protect user privacy effectively.In recent years,Tor’s congestion problem has become the focus of attention,and it can affect Tor’s performance even user experience.Firstly,we investigate the causes of Tor network congestion and summarize some link scheduling algorithms proposed in recent years.Then we propose the link scheduling algorithm SWRR based on WRR(Weighted Round Robin).In this process,we design multiple weight functions and compare the performance of these weight functions under different congestion conditions,and the appropriate weight function is selected to be used in our algorithms based on the experiment results.Finally,we also compare the performance of SWRR with other link scheduling algorithms under different congestion conditions by experiments,and verify the effectiveness of the algorithm SWRR.

大数据产业现状和挑战

系统地论述了大数据产业现状和挑战,介绍了大数据产业基本概念和范畴,分析了大数据产业生态中各环节,总结了大数据产业发展的政策环境以及投融资环境,最后,总结了大数据产业发展在数据共享、交易、隐私保护等方面面临的挑战。

A Traceable Capability-based Access Control for IoT

Delegation mechanism in Internet of Things(IoT)allows users to share some of their permissions with others.Cloud-based delegation solutions require that only the user who has registered in the cloud can be delegated permissions.It is not convenient when a permission is delegated to a large number of temporarily users.Therefore,some works like CapBAC delegate permissions locally in an offline way.However,this is difficult to revoke and modify the offline delegated permissions.In this work,we propose a traceable capability-based access control approach(TCAC)that can revoke and modify permissions by tracking the trajectories of permissions delegation.We define a time capability tree(TCT)that can automatically extract permissions trajectories,and we also design a new capability token to improve the permission verification,revocation and modification efficiency.The experiment results show that TCAC has less token verification and revocation/modification time than those of CapBAC and xDBAuth.TCAC can discover 73.3%unvisited users in the case of delegating and accessing randomly.This provides more information about the permissions delegation relationships,and opens up new possibilities to guarantee the global security in IoT delegation system.To the best of our knowledge,TCAC is the first work to capture the unvisited permissions.

A Material Identification Approach Based on Wi-Fi Signal

Material identification is a technology that can help to identify the type of target material.Existing approaches depend on expensive instruments,complicated pre-treatments and professional users.It is difficult to find a substantial yet effective material identification method to meet the daily use demands.In this paper,we introduce a Wi-Fi-signal based material identification approach by measuring the amplitude ratio and phase difference as the key features in the material classifier,which can significantly reduce the cost and guarantee a high level accuracy.In practical measurement of WiFi based material identification,these two features are commonly interrupted by the software/hardware noise of the channel state information(CSI).To eliminate the inherent noise of CSI,we design a denoising method based on the antenna array of the commercial off-the-shelf(COTS)Wi-Fi device.After that,the amplitude ratios and phase differences can be more stably utilized to classify the materials.We implement our system and evaluate its ability to identify materials in indoor environment.The result shows that our system can identify 10 commonly seen liquids with an average accuracy of 98.8%.It can also identify similar liquids with an overall accuracy higher than 95%,such as various concentrations of salt water.

Fed-DFE: A Decentralized Function Encryption-Based Privacy-Preserving Scheme for Federated Learning

Federated learning is a distributed learning framework which trains global models by passing model parameters instead of raw data.However,the training mechanism for passing model parameters is still threatened by gradient inversion,inference attacks,etc.With a lightweight encryption overhead,function encryption is a viable secure aggregation technique in federation learning,which is often used in combination with differential privacy.The function encryption in federal learning still has the following problems:a)Traditional function encryption usually requires a trust third party(TTP)to assign the keys.If a TTP colludes with a server,the security aggregation mechanism can be compromised.b)When using differential privacy in combination with function encryption,the evaluation metrics of incentive mechanisms in the traditional federal learning become invisible.In this paper,we propose a hybrid privacy-preserving scheme for federated learning,called Fed-DFE.Specifically,we present a decentralized multi-client function encryption algorithm.It replaces the TTP in traditional function encryption with an interactive key generation algorithm,avoiding the problem of collusion.Then,an embedded incentive mechanism is designed for function encryption.It models the real parameters in federated learning and finds a balance between privacy preservation and model accuracy.Subsequently,we implemented a prototype of Fed-DFE and evaluated the performance of decentralized function encryption algorithm.The experimental results demonstrate the effectiveness and efficiency of our scheme.

Long-Term Trend Analysis of Major Human Helminth Infections — Guangdong Province, China, 1988–2021

Introduction:Although helminth infections threaten millions of people worldwide,the spatiotemporal characteristics remain unclear across China.This study systematically describes the spatiotemporal changes of major human helminth infections and their epidemiological characteristics from 1988 to 2021 in Guangdong Province,China.Methods:The survey data in Guangdong Province were primarily obtained from 3 national surveys implemented during 1988–1992,2001–2004,and 2014–2016,respectively,and from the China Information System for Disease Control and Prevention during 2019–2021.A modified Kato-Katz technique was used to detect parasite eggs in collected fecal samples.Results:The overall standardized infection rates(SIRs)of any soil-transmitted helminths(STH)and Clonorchis sinensis decreased from 65.27%during 1988–1992 to 4.23%during 2019–2021.In particular,the SIRs of STH had even more of a decrease,from 64.41%during 1988–1992 to 0.31%during 2019–2021.The SIRs of Clonorchis sinensis in the 4 surveys were 2.40%,12.17%,5.20%,and 3.93%,respectively.This study observed different permutations of gender,age,occupation,and education level on the SIRs of helminths.Conclusions:The infection rate of STH has substantially decreased.However,the infection rate of Clonorchis sinensis has had fewer changes,and it has become the dominant helminth.

Cell electrospinning and its application in wound healing:principles,techniques and prospects

Currently,clinical strategies for the treatment of wounds are limited,especially in terms of achieving rapid wound healing.In recent years,based on the technique of electrospinning(ES),cell electrospinning(C-ES)has been developed to better repair related tissues or organs(such as skin,fat and muscle)by encapsulating living cells in a microfiber or nanofiber environment and constructing 3D living fiber scaffolds.Therefore,C-ES has promising prospects for promoting wound healing.In this article,C-ES technology and its advantages,the differences between CES and traditional ES,the parameters suitable for maintaining cytoactivity,and material selection and design issues are summarized.In addition,we review the application of C-ES in the fields of biomaterials and cells.Finally,the limitations and improved methods of C-ES are discussed.In conclusion,the potential advantages,limitations and prospects of C-ES application in wound healing are presented.

TPRPF:a preserving framework of privacy relations based on adversarial training for texts in big data

1Introduction Texts data are used to train deep learning models in cloud servers that have the strong computing power and large storage space,which can seriously endanger the user's privacy.Specifically,the attacker can use the intercepted text representations of text data of the primary learning tasks to train an adversarial classifier to infer private attributes or private information,such as gender,age of the user,and the relation between users.