Home appliance manufacturers strive to obtain feedback from users to improve their products and services to build a smart home system. To help manufacturers develop a smart home system, we design a federated learning (FL) system leveraging a reputation mechanism to assist home appliance manufacturers to train a machine learning model based on customers’ data. Then, manufacturers can predict customers’ requirements and consumption behaviors in the future. The working flow of the system includes two stages: in the first stage, customers train the initial model provided by the manufacturer using both the mobile phone and the mobile-edge computing (MEC) server. Customers collect data from various home appliances using phones, and then they download and train the initial model with their local data. After deriving local models, customers sign on their models and send them to the blockchain. In case customers or manufacturers are malicious, we use the blockchain to replace the centralized aggregator in the traditional FL system. Since records on the blockchain are untampered, malicious customers or manufacturers’ activities are traceable. In the second stage, manufacturers select customers or organizations as miners for calculating the averaged model using received models from customers. By the end of the crowdsourcing task, one of the miners, who is selected as the temporary leader, uploads the model to the blockchain. To protect customers’ privacy and improve the test accuracy, we enforce differential privacy (DP) on the extracted features and propose a new normalization technique. We experimentally demonstrate that our normalization technique outperforms batch normalization when features are under DP protection. In addition, to attract more customers to participate in the crowdsourcing FL task, we design an incentive mechanism to award participants.

Privacy-Preserving Blockchain-Based Federated Learning for IoT Devices

Differential privacy is a strong notion for privacy that can be used to prove formal guarantees, in terms of a privacy budget, $\epsilon$, about how much information is leaked by a mechanism. However, implementations of privacy-preserving machine learning often select large values of $\epsilon$ in order to get acceptable utility of the model, with little understanding of the impact of such choices on meaningful privacy. Moreover, in scenarios where iterative learning procedures are used, differential privacy variants that offer tighter analyses are used which appear to reduce the needed privacy budget but present poorly understood trade-offs between privacy and utility. In this paper, we quantify the impact of these choices on privacy in experiments with logistic regression and neural network models. Our main finding is that there is a huge gap between the upper bounds on privacy loss that can be guaranteed, even with advanced mechanisms, and the effective privacy loss that can be measured using current inference attacks. Current mechanisms for differentially private machine learning rarely offer acceptable utility-privacy trade-offs with guarantees for complex learning tasks: settings that provide limited accuracy loss provide meaningless privacy guarantees, and settings that provide strong privacy guarantees result in useless models. Code for the experiments can be found here: this https URL

Evaluating Differentially Private Machine Learning in Practice

Lane detection in driving scenes is an important module for autonomous vehicles and advanced driver assistance systems. In recent years, many sophisticated lane detection methods have been proposed. However, most methods focus on detecting the lane from one single image, and often lead to unsatisfactory performance in handling some extremely-bad situations such as heavy shadow, severe mark degradation, serious vehicle occlusion, and so on. In fact, lanes are continuous line structures on the road. Consequently, the lane that cannot be accurately detected in one current frame may potentially be inferred out by incorporating information of previous frames. To this end, we investigate lane detection by using multiple frames of a continuous driving scene, and propose a hybrid deep architecture by combining the convolutional neural network (CNN) and the recurrent neural network (RNN). Specifically, information of each frame is abstracted by a CNN block, and the CNN features of multiple continuous frames, holding the property of time-series, are then fed into the RNN block for feature learning and lane prediction. Extensive experiments on two large-scale datasets demonstrate that, the proposed method outperforms the competing methods in lane detection, especially in handling difficult situations.

Robust Lane Detection from Continuous Driving Scenes Using Deep Neural Networks

High-dimensional crowdsourced data collected from numerous users produces rich knowledge about our society; however, it also brings unprecedented privacy threats to the participants. Local differential privacy (LDP), a variant of differential privacy, is recently proposed as a state-of-the-art privacy notion. Unfortunately, achieving LDP on high-dimensional crowdsourced data publication raises great challenges in terms of both computational efficiency and data utility. To this end, based on the expectation maximization (EM) algorithm and Lasso regression, we first propose efficient multi-dimensional joint distribution estimation algorithms with LDP. Then, we develop a local differentially private high-dimensional data publication algorithm ( LoPub ) by taking advantage of our distribution estimation techniques. In particular, correlations among multiple attributes are identified to reduce the dimensionality of crowdsourced data, thus speeding up the distribution learning process and achieving high data utility. Extensive experiments on real-world datasets demonstrate that our multivariate distribution estimation scheme significantly outperforms existing estimation schemes in terms of both communication overhead and estimation speed. Moreover, LoPub can keep, on average, 80% and 60% accuracy over the released datasets in terms of support vector machine and random forest classification, respectively.

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$\textsf{LoPub}$ : High-Dimensional Crowdsourced Data Publication With Local Differential Privacy

Location information of workers are usually required for optimal task allocation in mobile crowdsensing, which however raises severe concerns of location privacy leakage. Although many approaches have been proposed to protect the locations of users, the location protection for task allocation in mobile crowdsensing has not been well explored. In addition, to the best of our knowledge, none of existing privacy-preserving task allocation mechanisms can provide personalized location protection considering different protection demands of workers. In this paper, we propose a personalized privacy-preserving task allocation framework for mobile crowdsensing that can allocate tasks effectively while providing personalized location privacy protection. The basic idea is that each worker uploads the obfuscated distances and personal privacy level to the server instead of its true locations or distances to tasks. In particular, we propose a Probabilistic Winner Selection Mechanism (PWSM) to minimize the total travel distance with the obfuscated information from workers, by allocating each task to the worker who has the largest probability of being closest to it. Moreover, we propose a Vickrey Payment Determination Mechanism (VPDM) to determine the appropriate payment to each winner by considering its movement cost and privacy level, which satisfies the truthfulness, profitability, and probabilistic individual rationality. Extensive experiments on the real-world datasets demonstrate the effectiveness of the proposed mechanisms.

Personalized Privacy-Preserving Task Allocation for Mobile Crowdsensing

Nowadays gigantic crowd-sourced data from mobile devices have become widely available in social networks, enabling the possibility of many important data mining applications to improve the quality of our daily lives. While providing tremendous benefits, the release of crowd-sourced social network data to the public will pose considerable threats to mobile users’ privacy. In this paper, we investigate the problem of real-time spatio-temporal data publishing in social networks with privacy preservation. Specifically, we consider continuous publication of population statistics and design RescueDP—an online aggregate monitoring framework over infinite streams with $w$ -event privacy guarantee. Its key components including adaptive sampling, adaptive budget allocation, dynamic grouping, perturbation and filtering, are seamlessly integrated as a whole to provide privacy-preserving statistics publishing on infinite time stamps. Moreover, we further propose an enhanced RescueDP with neural networks to accurately predict the values of statistics and improve the utility of released data. Both RescueDP and the enhanced RescueDP are proved satisfying $w$ -event privacy. We evaluate the proposed schemes with real-world as well as synthetic datasets and compare them with two $w$ -event privacy-assured representative methods. Experimental results show that the proposed schemes outperform the existing methods and improve the utility of real-time data sharing with strong privacy guarantee.

Real-Time and Spatio-Temporal Crowd-Sourced Social Network Data Publishing with Differential Privacy

With powerful parallel computing GPUs and massive user data, neural-network-based deep learning can well exert its strong power in problem modeling and solving, and has archived great success in many applications such as image classification, speech recognition and machine translation etc. While deep learning has been increasingly popular, the problem of privacy leakage becomes more and more urgent. Given the fact that the training data may contain highly sensitive information, e.g., personal medical records, directly sharing them among the users (i.e., participants) or centrally storing them in one single location may pose a considerable threat to user privacy. In this paper, we present a practical privacy-preserving collaborative deep learning system that allows users to cooperatively build a collective deep learning model with data of all participants, without direct data sharing and central data storage. In our system, each participant trains a local model with their own data and only shares model parameters with the others. To further avoid potential privacy leakage from sharing model parameters, we use functional mechanism to perturb the objective function of the neural network in the training process to achieve $\epsilon $ -differential privacy. In particular, for the first time, we consider the existence of unreliable participants , i.e., the participants with low-quality data, and propose a solution to reduce the impact of these participants while protecting their privacy. We evaluate the performance of our system on two well-known real-world datasets for regression and classification tasks. The results demonstrate that the proposed system is robust against unreliable participants, and achieves high accuracy close to the model trained in a traditional centralized manner while ensuring rigorous privacy protection.

Privacy-Preserving Collaborative Deep Learning With Unreliable Participants

Nowadays gigantic crowd-sourced data collected from mobile phone users have become widely available, which enables the possibility of many important data mining applications to improve the quality of our daily lives. While providing tremendous benefits, the release of these data to the public will pose a considerable threat to mobile users' privacy. To solve this problem, the notion of differential privacy has been proposed to provide privacy with theoretical guarantee, and recently it has been applied in streaming data publishing. However, most of the existing literature focus on either event-level privacy on infinite streams or user-level privacy on finite streams. In this paper, we investigate the problem of real-time spatiotemporal crowd-sourced data publishing with privacy preservation. Specifically, we consider continuous publication of population statistics for monitoring purposes and design RescueDP — an online aggregate monitoring scheme over infinite streams with privacy guarantee. RescueDP's key components include adaptive sampling, adaptive budget allocation, dynamic grouping, perturbation and filtering, which are seamlessly integrated as a whole to provide privacy-preserving statistics publishing on infinite time stamps. We show that RescueDP can achieve w-event privacy over data generated and published periodically by crowd users. We evaluate our scheme with real-world as well as synthetic datasets and compare it with two w-event privacy-assured representative benchmarks. Experimental results show that our solution outperforms the existing methods and improves the utility with strong privacy guarantee.

RescueDP: Real-time spatio-temporal crowd-sourced data publishing with differential privacy

With powerful parallel computing GPUs and massive user data, neural-network-based deep learning can well exert its strong power in problem modeling and solving, and has archived great success in many applications such as image classification, speech recognition and machine translation etc. While deep learning has been increasingly popular, the problem of privacy leakage becomes more and more urgent. Given the fact that the training data may contain highly sensitive information, e.g., personal medical records, directly sharing them among the users (i.e., participants) or centrally storing them in one single location may pose a considerable threat to user privacy. 
In this paper, we present a practical privacy-preserving collaborative deep learning system that allows users to cooperatively build a collective deep learning model with data of all participants, without direct data sharing and central data storage. In our system, each participant trains a local model with their own data and only shares model parameters with the others. To further avoid potential privacy leakage from sharing model parameters, we use functional mechanism to perturb the objective function of the neural network in the training process to achieve $\epsilon$-differential privacy. In particular, for the first time, we consider the existence of~\textit{unreliable participants}, i.e., the participants with low-quality data, and propose a solution to reduce the impact of these participants while protecting their privacy. We evaluate the performance of our system on two well-known real-world datasets for regression and classification tasks. The results demonstrate that the proposed system is robust against unreliable participants, and achieves high accuracy close to the model trained in a traditional centralized manner while ensuring rigorous privacy protection.

Privacy-Preserving Collaborative Deep Learning with Unreliable Participants

Fungal keratitis is a corneal infection, which severely impairs vision. The fungal pathogen provokes host immune response, but the excessive inflammatory response causes significant collateral damage to the cornea. Eugenol, the main component of clove oil, has been found to have a broad range of pharmacological activities including anti-microbial, antioxidation and anti-inflammation. However, the role of eugenol in Aspergillus fumigatus (A. fumigatus) keratitis is unknown. In this study, we demonstrated that eugenol reduced mice keratitis severity, inflammatory cells infiltration, pro-inflammatory cytokine expression, and the fungal load. Eugenol also decreased the expressions of pro-inflammatory cytokines in human corneal epithelial cells (HCECs). We confirmed that the anti-inflammatory effects of eugenol were related to activating nuclear factor erythroid 2-related factor 2/Heme Oxygenase-1 (Nrf2/HO-1) signaling pathway. Moreover, we demonstrated that eugenol could inhibit the A. fumigatus growth and adhesion to host cells, as well as damage the fungal biofilm. The antifungal mechanism seemed to be disrupting the integrity of the fungal membrane and reducing the biosynthesis of ergosterol. Taken together, our research suggested that eugenol exerted protective effects on mouse A. fumigatus keratitis, due to its anti-inflammatory and antifungal activity. 

Yan Zhang

Papers

Real-Time and Spatio-Temporal Crowd-Sourced Social Network Data Publishing with Differential Privacy

Privacy-Preserving Collaborative Deep Learning With Unreliable Participants

RescueDP: Real-time spatio-temporal crowd-sourced data publishing with differential privacy

Privacy-Preserving Collaborative Deep Learning with Unreliable Participants

Eugenol protects against Aspergillus fumigatus keratitis by inhibiting inflammatory response and reducing fungal load.