Privacy-preserving Method Research Articles

An Efficient and Differential Privacy-Based Scheme for Aggregating Mobility Datasets

Mobile smart devices, such as mobile phones, wearable devices, and in-vehicle navigation systems, bring us convenience and have become necessities in modern daily life. The built-in global positioning system (GPS) of these mobile devices collects the users’ mobility data to support path planning, navigation and other location-related applications, which also inevitably causes privacy issues. Previous research has shown that employing count-min sketch (CMS) to aggregate mobility datasets is a valid privacy-preserving method for resisting the reconstruction attack on population distributions. However, as the utility/accessibility of the protected datasets is excessively correlated with the size of CMS, decreasing the data transmission cost has become an unsolved issue of that approach. In this paper, we propose an efficient scheme with differential privacy to protect mobility datasets, which releases the privacy-preserving population distributions and achieves better utility as well as a much smaller data transmission cost compared to the CMS-based method. Our proposed scheme is comprised of two collaborative components, global sketch and temporal sketch. The global sketch is responsible for aggregating the raw mobility data and decreasing the data transmission cost, while the temporal sketch is in charge of guaranteeing the utility of the population distributions aggregated by the global sketch. Besides, to enhance the privacy preservation, we employ the Laplace mechanism to make the transmitted data satisfy ϵ-differential privacy. Through our analysis and empirical experiments, compared to the other three state-of-the-art privacy-preserving methods on mobility datasets, our scheme could preserve the privacy of the mobility datasets with much less data transmission cost under the same utility loss.

A Bitcoin-based Secure Outsourcing Scheme for Optimization Problem in Multimedia Internet of Things

With the development of the Internet of Things (IoT) and cloud computing, various multimedia data such as audio, video, and images have experienced explosive growth, ushering in the era of big data. Large-scale computing tasks in the Multimedia Internet of Things (M-IoT), such as mathematical optimization problems, have begun to be outsourced from IoT devices with limited computing power to cloud servers for execution. However, outsourcing computation brings security concerns, because the behaviors of clouds are invisible to users. The leakage of privacy data in outsourced optimization problems leads to immeasurable losses. The mutual distrust between clouds and users causes that the correctness of the optimal decisions and the fairness of the payment activities are not guaranteed. Blockchain technology has the characteristic of immutability and has become a new security paradigm for eliminating multi-party trust concerns. In this article, we propose a Bitcoin-based secure outsourcing scheme to address the aforementioned security concerns. To prevent confidential data leakage, the proposed scheme designs a computable privacy-preserving method for the outsourced optimization problems. To judge the correctness of the optimal decision and reduce verification costs, the proposed scheme designs a low-cost two-layer verification mechanism based on dual theory and blockchain technology. Blockchain nodes reach a consensus on the problem solutions and trigger an automatic fair payment protocol-based Bitcoin. Security analysis and experimental results demonstrate that our scheme guarantees privacy, fairness, and computational efficiency.

Privacy-Preserving Neural Networks for Smart Manufacturing

Abstract The rapid advance in sensing technology has expedited data-driven innovation in manufacturing by enabling the collection of large amounts of data from factories. Big data provides an unprecedented opportunity for smart decision-making in the manufacturing process. However, big data also attracts cyberattacks and makes manufacturing systems vulnerable due to the inherent value of sensitive information. The increasing integration of artificial intelligence (AI) within smart factories also exposes manufacturing equipment susceptible to cyber threats, posing a critical risk to the integrity of smart manufacturing systems. Cyberattacks targeting manufacturing data can result in considerable financial losses and severe business disruption. Therefore, there is an urgent need to develop AI models that incorporate privacy-preserving methods to protect sensitive information implicit in the models against model inversion attacks. Hence, this paper presents the development of a new approach called mosaic neuron perturbation (MNP) to preserve latent information in the framework of the AI model, ensuring differential privacy requirements while mitigating the risk of model inversion attacks. MNP is flexible to implement into AI models, balancing the trade-off between model performance and robustness against cyberattacks while being highly scalable for large-scale computing. Experimental results, based on real-world manufacturing data collected from the computer numerical control (CNC) turning process, demonstrate that the proposed method significantly improves the ability to prevent inversion attacks while maintaining high prediction performance. The MNP method shows strong potential for making manufacturing systems both smart and secure by addressing the risk of data breaches while preserving the quality of AI models.

Accident reduction through a privacy-preserving method on top of a novel ontology for autonomous vehicles with the support of modular arithmetic

Cloud of Things (CoT) emerges as a pivotal paradigm, connecting Internet of Things (IoT) devices to the Cloud Computing space, facilitating the efficient management of smart cities. In navigating the intricate landscape of smart city environments, this paper confronts two paramount challenges— heterogeneity and privacy preservation. Heterogeneity, rooted in the diverse origins of CoT devices from various vendors, intro- duces compatibility gaps and data format variations, impeding seamless communication among devices. Simultaneously, privacy preservation concerns itself with averting the inadvertent disclo- sure of sensitive data generated by CoT devices. Existing solutions often exhibit limitations in effectively addressing both challenges concurrently. To bridge this gap, our proposed solution employs a novel ontology-based approach, commencing with the introduc- tion of a groundbreaking ”Ontology” using the Protege software. This foundational tool serves a dual purpose—standardizing and unifying general and privacy-related information among diverse CoT devices. The ontology addresses the heterogeneity challenge by fostering a shared understanding and vocabulary, promoting interoperability for smoother communication among disparate devices. Complementing the ontology, a privacy-preservation method, implemented with ”MININET-WIFI” and grounded in Modular Arithmetic, dynamically adjusts the privacy-preserving rules of each CoT device. This adaptive mechanism signifi- cantly enhances security, mitigating the risk of unintentional data disclosure—a critical aspect evaluated extensively within the context of a widely used CoT application, specifically, the Autonomous Vehicle (AV) environment. The computational cost is meticulously evaluated, showcasing that our solution introduces a modest overhead, notably below 1.8 s, compared to alternative models. Furthermore, the penetration rate analysis reveals the solution's resilience against honest but curious Remote Service Units (RSUs). Communication overhead is quantified for various privacy-preserving methods, providing a comprehensive view of the solution's performance. Through rigorous simula- tions, encompassing assessments of communication overhead, computational costs, and penetration rates, our solution exhibits not only affordability for a diverse array of CoT devices in smart cities but also heightened resilience against malicious activities and adversaries, surpassing current studies. This paper, therefore, not only presents a novel ontology-based solution but also delves into the nuanced intricacies of heterogeneity and privacy preservation within CoT-based smart cities. The proposed approach, characterized by its dual focus on standardization and dynamic privacy adaptation, signifies a significant stride towards fostering secure, interoperable, and privacy-aware CoT ecosystems amid the dynamic landscape of smart cities.

Benchmarking robustness and privacy-preserving methods in federated learning

Federated learning (FL) is a machine learning framework that enables the use of user data for training without the need to share the data with the central server. FL's decentralized structure can lead to security and privacy issues, as it allows malicious or curious users to potentially participate in the training process. One limitation of most defense methods presented in FL is that they typically focus on only one aspect, either security or privacy. Therefore, the unintended effects of defensive methods in one field on another field are not clear. The purpose of this article is to examine security and privacy threats and defensive strategies in FL. In addition, the article investigates the performance of seven robust aggregators against three security attacks in both IID and non-IID settings. Furthermore, the impact of security and privacy defensive methods on each other is explored in the remainder of the article. To investigate the effect of security methods on the success rate of privacy attacks, the performance of seven robust aggregation methods against the membership inference attack is studied. The experiments reveal that the degree of privacy leakage is inversely related to the aggregator's robustness to security attacks. In other words, the greater the aggregator algorithm's robustness to security attacks, the greater the risk of privacy leakage. The impact of privacy-preserving methods on the performance of robust aggregation algorithms was investigated by studying the effect of the adversarial regularization method on their performance. The results indicate that while the adversarial regularization method can help protect user data privacy in FL, it can also disrupt the performance of robust aggregation methods. This can make it difficult for aggregators to accurately identify malicious users and reduce the overall accuracy of the global model.

Efficient federated learning privacy preservation method with heterogeneous differential privacy

Federated learning (FL) is a distributed machine learning method that effectively protects personal data. Many studies on federated learning assumed that all clients have consistent privacy parameters. However, in practice, different clients have different privacy requirements, and heterogeneous differential privacy can personalize privacy protection according to each client's privacy budget and requirements. In this study, we propose an improved efficient FL privacy preservation method with heterogeneous differential privacy, which can compute the corresponding privacy budget weights for each client according to noise size using the secure differential privacy stochastic gradient descent protocol, histogram of oriented gradients feature extraction and weighted averaging of the heterogeneous privacy budgets. Through this method, the noisier clients are given smaller privacy budgets weights to mitigate their negative impact on the aggregation model. Experiments comparing the baseline method were performed on the MNIST, fMNIST and cifar10 datasets. More precisely, the experimental results showed that our method improves the model accuracy by 6.68% and 7.18% of 20 to 50 clients and 16.08% and 17.37% of 60 to 100 clients, respectively. Moreover, the communication overhead time was reduced by 23.85%, which validates the effectiveness and usability of the proposed method.

A Dropout-Tolerated Privacy-Preserving Method for Decentralized Crowdsourced Federated Learning

A Dropout-Tolerated Privacy-Preserving Method for Decentralized Crowdsourced Federated Learning

Context-Aware Hybrid Encoding for Privacy-Preserving Computation in IoT Devices

Recent years have witnessed a surge in hybrid IoT-cloud applications where an end user distributes the desired computation between the IoT and cloud nodes. While achieving significant speed up, the major caveat of this approach is data privacy. Privacy-preserving methods have received major attention in the past few years, mainly because they can potentially solve this issue. Among several proposals, methods based on dynamic encoding and perturbation offer flexibility and low overhead. However, they often consider a weak adversary model or overlook practical limitations such as encoding latency and complexity. This work proposes a new privacy-preserving method to address these issues. The key contributions of this paper are twofold. First, unlike state-of-the-art, it proposes a new approach based on evolutionary algorithms to systematically evaluate the robustness of the encoding algorithm against a large population of potential adversaries. Second, it develops a dynamic obfuscation strategy that balances latency requirements in a realistic IoT-cloud hybrid ecosystem and privacy demands. Additionally, our method offers a unique benefit: it can be used alone for privacy protection, or it can be integrated with most existing methods to enhance privacy and reduce latency. The applicability and effectiveness of our proposed methods are thoroughly evaluated using two popular deep neural networks in a real-world IoT-cloud setting. We study the impact of our approach on important metrics, such as accuracy and privacy. Our results show that our proposed method can improve the overall privacy of a given IoT-cloud hybrid ecosystem by more than 10% on average.

Investigating the use of machine learning algorithms to support risk-based animal welfare inspections of cattle and pig farms.

In livestock production, animal-related data are often registered in specialised databases and are usually not interconnected, except for a common identifier. Analysis of combined datasets and the possible inclusion of third-party information can provide a more complete picture or reveal complex relationships. The aim of this study was to develop a risk index to predict farms with an increased likelihood for animal welfare violations, defined as non-compliance during on-farm welfare inspections. A data-driven approach was chosen for this purpose, focusing on the combination of existing Swiss government databases and registers. Individual animal-level data were aggregated at the herd level. Since data collection and availability were best for cattle and pigs, the focus was on these two livestock species. We present machine learning models that can be used as a tool to plan and optimise risk-based on-farm welfare inspections by proposing a consolidated list of priority holdings to be visited. The results of previous on-farm welfare inspections were used to calibrate a binary welfare index, which is the prediction goal. The risk index is based on proxy information, such as the participation in animal welfare programmes with structured housing and outdoor access, herd type and size, or animal movement data. Since transparency of the model is critical both for public acceptance of such a data-driven index and farm control planning, the Random Forest model, for which the decision process can be illustrated, was investigated in depth. Using historical inspection data with an overall low prevalence of violations of approximately 4% for both species, the developed index was able to predict violations with a sensitivity of 81.2 and 79.5% for cattle and pig farms, respectively. The study has shown that combining multiple and heterogeneous data sources improves the quality of the models. Furthermore, privacy-preserving methods are applied to a research environment to explore the available data before restricting the feature space to the most relevant. This study demonstrates that data-driven monitoring of livestock populations is already possible with the existing datasets and the models developed can be a useful tool to plan and conduct risk-based animal welfare inspection.

Data Valuation for Vertical Federated Learning: A Model-free and Privacy-preserving Method

Data Valuation for Vertical Federated Learning: A Model-free and Privacy-preserving Method

A Privacy Preservation Method for Attributed Social Network Based on Negative Representation of Information

A Privacy Preservation Method for Attributed Social Network Based on Negative Representation of Information

A Source Location Privacy Preservation Method Using Mixed Fake Sources and Phantoms

A Source Location Privacy Preservation Method Using Mixed Fake Sources and Phantoms

A Lightweight and Accuracy-Lossless Privacy-Preserving Method in Federated Learning

A Lightweight and Accuracy-Lossless Privacy-Preserving Method in Federated Learning

PRIVIC: A privacy-preserving method for incremental collection of location data

With recent advancements in technology, the threats of privacy violations of individuals' sensitive data are surging. Location data, in particular, have been shown to carry a substantial amount of sensitive information. A standard method to mitigate the privacy risks for location data consists in adding noise to the true values to achieve geo-indistinguishability (geo-ind). However, geo-ind alone is not sufficient to cover all privacy concerns. In particular, isolated locations are not sufficiently protected by the state-of-the-art Laplace mechanism (LAP) for geo-ind. In this paper, we focus on a mechanism based on the Blahut-Arimoto algorithm (BA) from the rate-distortion theory. We show that BA, in addition to providing geo-ind, enforces an elastic metric that mitigates the problem of isolation. Furthermore, BA provides an optimal trade-off between information leakage and quality of service. We then proceed to study the utility of BA in terms of the statistics that can be derived from the reported data, focusing on the inference of the original distribution. To this purpose, we de-noise the reported data by applying the iterative Bayesian update (IBU), an instance of the expectation-maximization method. It turns out that BA and IBU are dual to each other, and as a result, they work well together, in the sense that the statistical utility of BA is quite good and better than LAP for high privacy levels. Exploiting these properties of BA and IBU, we propose an iterative method, PRIVIC, for a privacy-friendly incremental collection of location data from users by service providers. We illustrate the soundness and functionality of our method both analytically and with experiments.

Reversible data hiding in encrypted images with multi-prediction and adaptive huffman encoding

With the rapid development of multimedia technology and the massive accumulation of user data, a huge amount of data is rapidly generated and shared over the network, while the problems of inappropriate data access and abuse persist. Reversible data hiding in encrypted images (RDHEI) is a privacy-preserving method that embeds protected data in an encrypted domain and accurately extracts the embedded data without affecting the original content. However, the amount of embedded data has been one of the major limitations in the performance and application of RDHEI. Currently, the main approaches to improve the capacity of RDHEI are either to increase the overall capacity or to reduce the length of the auxiliary information. In this paper, we propose a novel RDHEI scheme based on multi-prediction and adaptive Huffman encoding. To increase the overall capacity, we propose a multi-prediction, called MED+GAP predictor, to generate the label map data of non-reference pixels prior to image encryption. Then, an adaptive Huffman coding is designed to compress the generated labels in order to reduce the embedding length of the auxiliary information used for the extraction and recovery. Experiments show that the proposed method with MED+GAP predictor and adaptive Huffman coding improves 0.052 bpp, 0.023 bpp, and 0.047 bpp on average over the other state-of-the-art methods on the BOSSBase, BOWS-2, and UCID datasets, respectively, while maintaining security and reversibility.

Secure Collaborative Computing for Linear Regression

Machine learning usually requires a large amount of training data to build useful models. We exploit the mathematical structure of linear regression to develop a secure and privacy-preserving method that allows multiple parties to collaboratively compute optimal model parameters without requiring the parties to share their raw data. The new approach also allows for efficient deletion of the data of users who want to leave the group and who wish to have their data deleted. Since the data remain confidential during both the learning and unlearning processes, data owners are more inclined to share the datasets they collect to improve the models, ultimately benefiting all participants. The proposed collaborative computation of linear regression models does not require a trusted third party, thereby avoiding the difficulty of building a robust trust system in the current Internet environment. The proposed scheme does not require encryption to keep the data secret, nor does it require the use of transformations to hide the real data. Instead, our scheme sends only the aggregated data to build a collaborative learning scheme. This makes the scheme more computationally efficient. Currently, almost all homomorphic encryption schemes that support both addition and multiplication operations demand significant computational resources and can only offer computational security. We prove that a malicious party lacks sufficient information to deduce the precise values of another party’s original data, thereby preserving the privacy and security of the data exchanges. We also show that the new linear regression learning scheme can be updated incrementally. New datasets can be easily incorporated into the system, and specific data can be removed to refine the linear regression model without the need to recompute from the beginning.

HN3S: A Federated AutoEncoder framework for Collaborative Filtering via Hybrid Negative Sampling and Secret Sharing

Federated recommender systems can serve users with suitable item recommendations while preserving their privacy, but most current works cannot serve non-participant users. We consider that Federated AutoEncoder for Collaborative Filtering (FAE-CF) is expected to solve this problem. However, FAE-CF encounters privacy protection concerns and high communication overhead issues. In this paper, we investigate FAE-CF’s privacy protection capabilities and communication volume, unveiling its potential privacy security issues and inferior practicality. By analyzing the FAE-CF gradient, we propose an effective attack that could recover most user data, with all recovery metrics above 95.53% in our simulation experiments. To address these challenges, we propose Hybrid Negative Sampling and Secret Sharing (HN3S), a privacy-preserving method designed for FAE-CF. HN3S uses hybrid negative sampling to reduce communication overhead and secret sharing to protect user data. Experiments conducted on various public datasets with multiple AE-CFs have shown that HN3S can effectively protect the privacy of FAE-CF, reducing the communication overhead by up to 60.44%, while maintaining the performance reduction within 10.78%. Our research reveals significant privacy risks and poor utility inherent in current FAE-CFs, and provides a feasible solution to protect user privacy and balance communication overhead with algorithm performance.

A Blockchain-Based Privacy-Preserving and Fair Data Transaction Model in IoT

The rapid development of the Internet of Things (IoT) has resulted in vast amounts of widely distributed data. Sharing these data can spur innovative advancements and enhance service quality. However, conventional data-sharing methods often involve third-party intermediaries, posing risks of single-point failures and privacy leaks. Moreover, these traditional sharing methods lack a secure transaction model to compensate for data sharing, which makes ensuring fair payment between data consumers and providers challenging. Blockchain, as a decentralized, secure, and trustworthy distributed ledger, offers a novel solution for data sharing. Nevertheless, since all nodes on the blockchain can access on-chain data, data privacy is inadequately protected, and traditional privacy-preserving methods like anonymization and generalization are ineffective against attackers with background knowledge. To address these issues, this paper proposes a decentralized, privacy-preserving, and fair data transaction model based on blockchain technology. We designed an adaptive local differential privacy algorithm, MDLDP, to protect the privacy of transaction data. Concurrently, verifiable encrypted signatures are employed to address the issue of fair payment during the data transaction process. This model proposes a committee structure to replace the individual arbitrator commonly seen in traditional verifiable encrypted signatures, thereby reducing potential collusion between dishonest traders and the arbitrator. The arbitration committee leverages threshold signature techniques to manage arbitration private keys. A full arbitration private key can only be collaboratively constructed by any arbitrary t members, ensuring the key’s security. Theoretical analyses and experimental results reveal that, in comparison to existing approaches, our model delivers enhanced transactional security. Moreover, while guaranteeing data availability, MDLDP affords elevated privacy protection.

Research on differential privacy protection method based on user tendency.

It is a new attack model to mine user's activity rule from user's massive data. In order to solve the privacy leakage problem caused by user tendency in current privacy preserving methods, an extended differential privacy preserving method based on user's tendency is proposed in the paper. By constructing a Markov chain, and using the Markov decision process, it equivalently expresses user's tendency as measurable state transition probability, which can transform qualitative descriptions of user's tendency into a quantitative representation to achieve an accurate measurement of the user tendency. An extended (P,ε)-differential privacy protection method is proposed in the work, by introducing a privacy model parameter R, it combines the quantified user's propensity probability with a differential privacy budget parameter, and it can dynamically add different noise amounts according to the user's tendency, so as to achieve the purpose of protecting the user's propensity privacy information and improve data availability. Finally, the feasibility and effectiveness of the proposed method was verified by experiments.

Federated Latent Dirichlet Allocation for User Preference Mining

In the field of Web services computing, a recent demand trend is to mine user preferences based on user requirements when creating Web service compositions, in order to meet comprehensive and ever evolving user needs. Machine learning methods such as the latent Dirichlet allocation (LDA) have been applied for user preference mining. However, training a high-quality LDA model typically requires large amounts of data. With the prevalence of government regulations and laws and the enhancement of people’s awareness of privacy protection, the traditional way of collecting user data on a central server is no longer applicable. Therefore, it is necessary to design a privacy-preserving method to train an LDA model without massive collecting or leaking data. In this paper, we present novel federated LDA techniques to learn user preferences in the Web service ecosystem. On the basis of a user-level distributed LDA algorithm, we establish two federated LDA models in charge of two-layer training scenarios: a centralized synchronous federated LDA (CSFed-LDA) for synchronous scenarios and a decentralized asynchronous federated LDA (DAFed-LDA) for asynchronous ones. In the former CSFed-LDA model, an importance-based partially homomorphic encryption (IPHE) technique is developed to protect privacy in an efficient manner. In the latter DAFed-LDA model, blockchain technology is incorporated and a multi-channel-based authority control scheme (MCACS) is designed to enhance data security. Extensive experiments over a real-world dataset ProgrammableWeb.com have demonstrated the model performance, security assurance and training speed of our approach.