With the emergence of ambient sensing technologies which combine mobile crowdsensing and Internet of Things,large amount of people-centric data can be obtained and utilized to build people-centric services.Note that t...With the emergence of ambient sensing technologies which combine mobile crowdsensing and Internet of Things,large amount of people-centric data can be obtained and utilized to build people-centric services.Note that the service quality is highly related to the privacy level of the data.In this paper,we investigate the problem of privacy-aware service subscription in people-centric sensing.An efficient resource allocation framework using a combinatorial auction(CA)model is provided.Specifically,the resource allocation problem that maximizes the social welfare in view of varying requirements of multiple users is formulated,and it is solved by a proposed computationally tractable solution algorithm.Furthermore,the prices of allocated resources that winners need to pay are figured out by a designed scheme.Numerical results demonstrate the effectiveness of the proposed scheme.展开更多
Recent advances in wearable devices have enabled large-scale collection of sensor data across healthcare,sports,and other domains but this has also raised critical privacy concerns,especially under tightening regulati...Recent advances in wearable devices have enabled large-scale collection of sensor data across healthcare,sports,and other domains but this has also raised critical privacy concerns,especially under tightening regulations such as the General Data Protection Regulation(GDPR),which explicitly restrict the processing of data that can re-identify individuals.Although existing anonymization approaches such as the AnonymizingAutoEncoder(AAE)can reduce the risk of re-identification,they often introduce substantial waveform distortions and fail to preserve information beyond a single classification task(e.g.,human activity recognition).This study proposes a novel sensor data anonymization method based onAdversarial Perturbations(AP)to address these limitations.By generating minimal yet targeted noise,the proposed method significantly degrades the accuracy of identity classification while retaining essential features for multiple tasks such as activity,gender,or device-position recognition.Moreover,to enhance robustness against frequency-domain analysis,additional models trained on transformed(e.g.,short-time Fourier transform(STFT))representations are incorporated into the perturbation process.A multi-task formulation is introduced that selectively suppresses person-identifying features while reinforcing those relevant to other desired tasks without retraining large autoencoder-based architectures.The proposed framework is,to our knowledge,the first AP-based anonymization technique that(i)defends simultaneously against time-and frequency-domain attacks and(ii)allows per-task trade-off control on a single forward-back-propagation run,enabling real-time,on-device deployment on commodity hardware.On three public datasets,the proposed method reduces person-identification accuracy from 60–90%to near-chance levels(≤5%)while preserving the original activity-recognition F1 both in the time and frequency domains.Compared with the baseline AAE,the proposed method improves downstream task F1 and lowers waveform mean squared error,demonstrating a better privacy-utility trade-off without additional model retraining.These findings underscore the effectiveness and flexibility of AP in privacy-preserving sensor-data processing,offering a practical solution that safeguards user identity while retaining rich,application-critical information.展开更多
Intelligent healthcare networks represent a significant component in digital applications,where the requirements hold within quality-of-service(QoS)reliability and safeguarding privacy.This paper addresses these requi...Intelligent healthcare networks represent a significant component in digital applications,where the requirements hold within quality-of-service(QoS)reliability and safeguarding privacy.This paper addresses these requirements through the integration of enabler paradigms,including federated learning(FL),cloud/edge computing,softwaredefined/virtualized networking infrastructure,and converged prediction algorithms.The study focuses on achieving reliability and efficiency in real-time prediction models,which depend on the interaction flows and network topology.In response to these challenges,we introduce a modified version of federated logistic regression(FLR)that takes into account convergence latencies and the accuracy of the final FL model within healthcare networks.To establish the FLR framework for mission-critical healthcare applications,we provide a comprehensive workflow in this paper,introducing framework setup,iterative round communications,and model evaluation/deployment.Our optimization process delves into the formulation of loss functions and gradients within the domain of federated optimization,which concludes with the generation of service experience batches for model deployment.To assess the practicality of our approach,we conducted experiments using a hypertension prediction model with data sourced from the 2019 annual dataset(Version 2.0.1)of the Korea Medical Panel Survey.Performance metrics,including end-to-end execution delays,model drop/delivery ratios,and final model accuracies,are captured and compared between the proposed FLR framework and other baseline schemes.Our study offers an FLR framework setup for the enhancement of real-time prediction modeling within intelligent healthcare networks,addressing the critical demands of QoS reliability and privacy preservation.展开更多
基金This work was partially supported by National Natural Science Foundation of China under Grant No.61801167Natural Science Foundation of Jiangsu Province of China under Grant No.BK20160874.
文摘With the emergence of ambient sensing technologies which combine mobile crowdsensing and Internet of Things,large amount of people-centric data can be obtained and utilized to build people-centric services.Note that the service quality is highly related to the privacy level of the data.In this paper,we investigate the problem of privacy-aware service subscription in people-centric sensing.An efficient resource allocation framework using a combinatorial auction(CA)model is provided.Specifically,the resource allocation problem that maximizes the social welfare in view of varying requirements of multiple users is formulated,and it is solved by a proposed computationally tractable solution algorithm.Furthermore,the prices of allocated resources that winners need to pay are figured out by a designed scheme.Numerical results demonstrate the effectiveness of the proposed scheme.
基金supported in part by the Japan Society for the Promotion of Science(JSPS)KAKENHI Grant-in-Aid for Scientific Research(C)under Grants 23K11164.
文摘Recent advances in wearable devices have enabled large-scale collection of sensor data across healthcare,sports,and other domains but this has also raised critical privacy concerns,especially under tightening regulations such as the General Data Protection Regulation(GDPR),which explicitly restrict the processing of data that can re-identify individuals.Although existing anonymization approaches such as the AnonymizingAutoEncoder(AAE)can reduce the risk of re-identification,they often introduce substantial waveform distortions and fail to preserve information beyond a single classification task(e.g.,human activity recognition).This study proposes a novel sensor data anonymization method based onAdversarial Perturbations(AP)to address these limitations.By generating minimal yet targeted noise,the proposed method significantly degrades the accuracy of identity classification while retaining essential features for multiple tasks such as activity,gender,or device-position recognition.Moreover,to enhance robustness against frequency-domain analysis,additional models trained on transformed(e.g.,short-time Fourier transform(STFT))representations are incorporated into the perturbation process.A multi-task formulation is introduced that selectively suppresses person-identifying features while reinforcing those relevant to other desired tasks without retraining large autoencoder-based architectures.The proposed framework is,to our knowledge,the first AP-based anonymization technique that(i)defends simultaneously against time-and frequency-domain attacks and(ii)allows per-task trade-off control on a single forward-back-propagation run,enabling real-time,on-device deployment on commodity hardware.On three public datasets,the proposed method reduces person-identification accuracy from 60–90%to near-chance levels(≤5%)while preserving the original activity-recognition F1 both in the time and frequency domains.Compared with the baseline AAE,the proposed method improves downstream task F1 and lowers waveform mean squared error,demonstrating a better privacy-utility trade-off without additional model retraining.These findings underscore the effectiveness and flexibility of AP in privacy-preserving sensor-data processing,offering a practical solution that safeguards user identity while retaining rich,application-critical information.
基金supported by Institute of Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)(No.RS2022-00167197Development of Intelligent 5G/6G Infrastructure Technology for the Smart City)+2 种基金in part by the National Research Foundation of Korea(NRF),Ministry of Education,through Basic Science Research Program under Grant NRF-2020R1I1A3066543in part by BK21 FOUR(Fostering Outstanding Universities for Research)under Grant 5199990914048in part by the Soonchunhyang University Research Fund.
文摘Intelligent healthcare networks represent a significant component in digital applications,where the requirements hold within quality-of-service(QoS)reliability and safeguarding privacy.This paper addresses these requirements through the integration of enabler paradigms,including federated learning(FL),cloud/edge computing,softwaredefined/virtualized networking infrastructure,and converged prediction algorithms.The study focuses on achieving reliability and efficiency in real-time prediction models,which depend on the interaction flows and network topology.In response to these challenges,we introduce a modified version of federated logistic regression(FLR)that takes into account convergence latencies and the accuracy of the final FL model within healthcare networks.To establish the FLR framework for mission-critical healthcare applications,we provide a comprehensive workflow in this paper,introducing framework setup,iterative round communications,and model evaluation/deployment.Our optimization process delves into the formulation of loss functions and gradients within the domain of federated optimization,which concludes with the generation of service experience batches for model deployment.To assess the practicality of our approach,we conducted experiments using a hypertension prediction model with data sourced from the 2019 annual dataset(Version 2.0.1)of the Korea Medical Panel Survey.Performance metrics,including end-to-end execution delays,model drop/delivery ratios,and final model accuracies,are captured and compared between the proposed FLR framework and other baseline schemes.Our study offers an FLR framework setup for the enhancement of real-time prediction modeling within intelligent healthcare networks,addressing the critical demands of QoS reliability and privacy preservation.
