Differential Privacy Protection Research Articles

Dual Attention Graph Convolutional Network optimized with the Sun Flower Optimization for Differential Privacy Protection Mechanism for Smart Grid against Security Attacks

A smart grid combines information and communication technology with the conventional power system to provide efficient and dependable electricity generation, transmission, distribution, and control. Utilities and users share information and electricity in a smart grid. This manuscript presents a Dual Attention Graph Convolutional Network (DAGCN) optimized with the Sun Flower Optimization Algorithm (SFO) predicting the differential privacy protection mechanism for smart grid (DPSG-DAGCN-SFO). Initially, the data is collected from NSL-KDD datasets. Afterward, the data is fed to an Adaptive Robust Cubature Kalman Filter (ARCKF) based preprocessing process. Then the preprocessed data’s are fed to Adaptive and Concise Empirical Wavelet Transform (ACEWT)for extracting features such as authentication, integrity and trusted authority. The extracted features are fed to Dual Attention Graph Convolutional Network (DAGCN)to classify the privacy protection in smart grid such as stealthy attack and no attack. The weight parameters of DAGCN are optimized using SFO. The proposed DPSG-DAGCN-SFO is implemented in python, effectiveness assessed by several performance metrics such as accuracy, error rate, precision, sensitivity, F1-score and recall. The proposed method error rate attains 2% stealthy attack and4% no attack of the of the privacy protection in smart grid. The proposed method shows better results in all existing systems like Bayesian Clustering Algorithm (BCA), Self-Adaptive Grid-Partitioning Algorithm (SGNA) and Elliptic Curve Digital Signature Algorithm (ECDSA).From the result it is concludes that the proposed DPSG-DAGCN-SFO method based error rate lower than the existing methods.

Matrix factorization recommender based on adaptive Gaussian differential privacy for implicit feedback

Matrix factorization (MF) is an effective technique in recommendation systems. Since MF needs to utilize and analyze large amounts of user data during the recommendation process, this may lead to the leakage of personal data. Most of the current privacy-preserving MF research aims to protect explicit feedback, but ignores the protection of implicit feedback. In response to this limitation, we propose an adaptive differentially private MF (ADPMF) for implicit feedback. The proposed model is trained under the framework of Bayesian personalized ranking and uses gradient perturbation to achieve the (ϵ,δ)-differential privacy. In our model, we design two effective methods, adaptive clipping and adaptive noise scale, to improve recommendation performance while maintaining privacy. We use Gaussian Differential Privacy (GDP) to accommodate privacy analysis for dynamically changing clipping thresholds and noise scale. Theoretical analysis and experimental results demonstrate that ADPMF not only achieves highly accurate recommendations but also provides differential privacy protection for implicit feedback. The results show that ADPMF can improve the recommended performance substantially by 10% to 20% compared to the current privacy-preserving recommendation methods and has promising application prospects in various fields.

Local Differentially Private Heavy Hitter Detection in Data Streams with Bounded Memory

Top-k frequent items detection is a fundamental task in data stream mining. Many promising solutions are proposed to improve memory efficiency while still maintaining high accuracy for detecting the Top-k items. Despite the memory efficiency concern, the users could suffer from privacy loss if participating in the task without proper protection, since their contributed local data streams may continually leak sensitive individual information. However, most existing works solely focus on addressing either the memory-efficiency problem or the privacy concerns but seldom jointly, which cannot achieve a satisfactory tradeoff between memory efficiency, privacy protection, and detection accuracy. In this paper, we present a novel framework HG-LDP to achieve accurate Top-k item detection at bounded memory expense, while providing rigorous local differential privacy (LDP) protection. Specifically, we identify two key challenges naturally arising in the task, which reveal that directly applying existing LDP techniques will lead to an inferior "accuracy-privacy-memory efficiency" tradeoff. Therefore, we instantiate three advanced schemes under the framework by designing novel LDP randomization methods, which address the hurdles caused by the large size of the item domain and by the limited space of the memory. We conduct comprehensive experiments on both synthetic and real-world datasets to show that the proposed advanced schemes achieve a superior "accuracy-privacy-memory efficiency" tradeoff, saving 2300× memory over baseline methods when the item domain size is 41,270. Our code is anonymously open-sourced via the link.

Statistical modelling under differential privacy constraints: a case study in fine-scale geographical analysis with Australian Bureau of Statistics TableBuilder data

ABSTRACT Consistent with the principles of differential privacy protection, the Australian Bureau of Statistics artificially perturbs all count data from the Australian Census prior to its release to researchers through the TableBuilder platform. This perturbation involves the addition of random noise to every non-zero cell count followed by the suppression of small values to zero. A consequent problem encountered by researchers working with geographical TableBuilder outputs is that the aggregate sums of cell counts over fine-scale units are often much lower than the direct query totals of their coarser enclosing units. Hierarchical Bayesian modelling is here proposed as a powerful methodology for achieving consistent multi-scale statistical reconstructions suitable for downstream spatial modelling. The utility of this framework is demonstrated for the toy problem of reconstructing Multiple Family Household counts by Mesh Block in the Perth Greater Capital City area.

DP-EPSO: Differential privacy protection algorithm based on differential evolution and particle swarm optimization

In deep learning differential privacy protection, adding noise based on gradient has become a mainstream algorithm, but excessive gradient perturbation noise causes accuracy degradation. To solve this problem, a differential privacy protection algorithm based on differential evolution and particle swarm optimization is proposed to realize hyperparameter optimization in differential privacy, reduce the impact of noise on the model, and effectively improve the accuracy. On the one hand, the differential evolution scheme performs selection, crossover and mutation on learning rate η, make it approach the global optimal solution, and improve the computational efficiency of the algorithm. On the other hand, the particle swarm optimization scheme is adopted. Without changing the parameters and gradient structure, the parameters are optimized by using the network propagation attributes, which reduces the influence of noise on the accuracy. Experiments are performed on three datasets: Cifar10, Mnist and FashionMnist. Compared with the existing differential privacy algorithms, under the same privacy budget, the proposed algorithm has better accuracy and higher efficiency.

A clustering-based differential privacy protection algorithm for weighted social networks.

Weighted social networks play a crucial role in various fields such as social media analysis, healthcare, and recommendation systems. However, with their widespread application and privacy issues have become increasingly prominent, including concerns related to sensitive information leakage, individual behavior analysis, and privacy attacks. Despite traditional differential privacy protection algorithms being able to protect privacy for edges with sensitive information, directly adding noise to edge weights may result in excessive noise, thereby reducing data utility. To address these challenges, we proposed a privacy protection algorithm for weighted social networks called DCDP. The algorithm combines the density clustering algorithm OPTICS to partition the weighted social network into multiple sub-clusters and adds noise to different sub-clusters at random sampling frequencies. To enhance the balance of privacy protection, we designed a novel privacy parameter calculation method. Through theoretical derivation and experimentation, the DCDP algorithm demonstrated its capability to achieve differential privacy protection for weighted social networks while effectively maintaining data accuracy. Compared to traditional privacy protection algorithms, the DCDP algorithm reduced the average relative error by approximately 20% and increases the proportion of unchanged shortest paths by about 10%. In summary, we aimed to address privacy issues in weighted social networks, providing an effective method to protect user-sensitive information while ensuring the accuracy and utility of data analysis.

Research on Local Fingerprint Image Differential Privacy Protection Method Based on Clustering Algorithm and Regression Algorithm Segmentation Image

Research on Local Fingerprint Image Differential Privacy Protection Method Based on Clustering Algorithm and Regression Algorithm Segmentation Image

Research on Fingerprint Image Differential Privacy Protection Publishing Method Based on Wavelet Transform and Singular Value Decomposition Technology

Research on Fingerprint Image Differential Privacy Protection Publishing Method Based on Wavelet Transform and Singular Value Decomposition Technology

Personalized trajectory differential privacy protection mechanism based on spatiotemporal correlation prediction

Personalized trajectory differential privacy protection mechanism based on spatiotemporal correlation prediction

Edge-DPSDG: An Edge-based Differential Privacy Protection Model for Smart Healthcare

Edge-DPSDG: An Edge-based Differential Privacy Protection Model for Smart Healthcare

Private Continuous Survival Analysis with Distributed Multi-Site Data.

Effective disease surveillance systems require large-scale epidemiological data to improve health outcomes and quality of care for the general population. As data may be limited within a single site, multi-site data (e.g., from a number of local/regional health systems) need to be considered. Leveraging distributed data across multiple sites for epidemiological analysis poses significant challenges. Due to the sensitive nature of epidemiological data, it is imperative to design distributed solutions that provide strong privacy protections. Current privacy solutions often assume a central site, which is responsible for aggregating the distributed data and applying privacy protection before sharing the results (e.g., aggregation via secure primitives and differential privacy for sharing aggregate results). However, identifying such a central site may be difficult in practice and relying on a central site may introduce potential vulnerabilities (e.g., single point of failure). Furthermore, to support clinical interventions and inform policy decisions in a timely manner, epidemiological analysis need to reflect dynamic changes in the data. Yet, existing distributed privacy-protecting approaches were largely designed for static data (e.g., one-time data sharing) and cannot fulfill dynamic data requirements. In this work, we propose a privacy-protecting approach that supports the sharing of dynamic epidemiological analysis and provides strong privacy protection in a decentralized manner. We apply our solution in continuous survival analysis using the Kaplan-Meier estimation model while providing differential privacy protection. Our evaluations on a real dataset containing COVID-19 cases show that our method provides highly usable results.

Mining frequent items from high-dimensional set-valued data under local differential privacy protection

Mining frequent items from high-dimensional set-valued data under local differential privacy protection

Personalized Privacy Protection Based on Space Grid in Mobile Crowdsensing

The rapid proliferation of handheld intelligent devices and the advent of 5G technology have brought about convenient and fast services for people. In perception-oriented application services, participating users will upload sensitive mobile data in order to obtain benefits. While devising privacy protection strategies to ensure data security, it is crucial to accomplish task perception related to data collection to the fullest extent possible. To address this challenge, this paper proposes a personalized data privacy protection algorithm based on an adaptive dynamic adjustment grid and the minimum wage task allocation strategy. According to the different levels of users’ needs for privacy protection, combined with the privacy budget allocation strategy, we design a different-level differential privacy protection mechanism and consider the reward mechanism in task allocation to balance the effectiveness and security of the location data uploaded by users. Experiments show that the strategy proposed in this paper can not only protect the data but also enable users to freely choose the level of privacy protection.

A Multi-Agent Deep-Reinforcement-Learning-Based Strategy for Safe Distributed Energy Resource Scheduling in Energy Hubs

An energy hub (EH) provides an effective solution to the management of local integrated energy systems (IES), supporting the optimal dispatch and mutual conversion of distributed energy resources (DER) in multi-energy forms. However, the intrinsic stochasticity of renewable generation intensifies fluctuations in the system’s energy production when integrated into large-scale grids and increases peak-to-valley differences in large-scale grid integration, leading to a significant reduction in the stability of the power grid. A distributed privacy-preserving energy scheduling method based on multi-agent deep reinforcement learning is presented for the EH cluster with renewable energy generation. Firstly, each EH is treated as an agent, transforming the energy scheduling problem into a Markov decision process. Secondly, the objective function is defined as minimizing the total economic cost while considering carbon trading costs, guiding the agents to make low-carbon decisions. Lastly, differential privacy protection is applied to sensitive data within the EH, where noise is introduced using energy storage systems to maintain the same gas and electricity purchases while blurring the original data. The experimental simulation results demonstrate that the agents are able to train and learn from environmental information, generating real-time optimized strategies to effectively handle the uncertainty of renewable energy. Furthermore, after the noise injection, the validity of the original data is compromised while ensuring the protection of sensitive information.

Entropy-driven differential privacy protection scheme based on social graphlet attributes

Entropy-driven differential privacy protection scheme based on social graphlet attributes

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.

Performance Evaluation of Data Utility for a Differential Privacy Scheme Supporting Fault Tolerance

The evolution of smart grids improves the sustainability, controllability, stability, and efficiency of traditional power grids. There is a challenging issue in smart grids with protecting users’ privacy while collecting and controlling individual fine-grained data. To ensure data integrity and address the privacy issue, differential privacy protection is an efficient method to resist differential attacks on aggregated data. However, due to differential noise and faulty smart meters, the problem of differential noise deviation has a great impact on the utility of aggregated data. In this paper, we further supplement the previous work by improving the prediction method, forming a relatively complete DP protection scheme (DPP-UFT) with fault tolerance, and providing a detailed performance evaluation process. The experimental results show that the proposed method of adding differential noise based on the estimated failure rate is related to the estimated failure rate and the noise factor. Compared with several other related literature, it has achieved a higher data utilization effect.

A study of user data privacy protection algorithms in the context of metaverse based on emotional AI IoT

Abstract In the context of the metaverse, user data privacy protection has become an important issue. In this paper, firstly, a user data privacy leakage risk assessment scheme is designed by attribute sensitivity calculation, attribute similarity calculation, and attribute association calculation. Then a data privacy protection algorithm based on differential privacy is proposed, and the differential privacy data protection algorithm and implementation mechanism are described. Finally, the performance of the differential privacy protection algorithm is evaluated by analyzing the learning performance and protection performance of the algorithm. The results show that the learning performance of the differential privacy protection model decreases with increasing τ when q = 0 The larger q is, the better the protection performance of the model is, and the optimal τ value also shows a trend to the right. This study provides an effective method for user data privacy protection under the metaverse and offers new ideas for research in related fields.

Application of clustering cooperative differential privacy in spatial crowdsourcing task allocation

A framework for spatial crowdsourcing task allocation based on centralized differential privacy is proposed for addressing the problem of worker’s location privacy leakage. Firstly, by combining two stages of differential privacy noise addition and clustering matching, a spatial crowdsourcing worker dataset with high differential privacy protection can be obtained; Secondly, the dynamic problem of spatial crowdsourcing task allocation is transformed into a static combinatorial optimization problem by dividing the spatiotemporal units and the “delay matching” strategy; Finally, the improved discrete glowworm swarm optimization algorithm is used to calculate the results of spatial crowdsourcing task allocation. It has been demonstrated that, compared to the direct differential privacy noise-adding assignment method and the discrete glowworm swarm optimization assignment method, the proposed method achieves better task assignment results, with the total travel distance reduced by 12.42% and 3.56%, respectively, and the task assignment success rate increased by 11.75% and 3.34%, respectively.

Breast Cancer Prediction Based on Differential Privacy and Logistic Regression Optimization Model

In order to improve the classification effect of the logistic regression (LR) model for breast cancer prediction, a new hybrid feature selection method is proposed to process the data, using the Pearson correlation test and the iterative random forest algorithm based on out-of-bag estimation (RF-OOB) to screen the optimal 17 features as inputs to the model. Secondly, the LR is optimized using the batch gradient descent (BGD-LR) algorithm to train the loss function of the model to minimize the loss. In order to protect the privacy of breast cancer patients, a differential privacy protection technology is added to the BGD-LR model, and an LR optimization model based on differential privacy with batch gradient descent (BDP-LR) is constructed. Finally, experiments are carried out on the Wisconsin Diagnostic Breast Cancer (WDBC) dataset. Meanwhile, accuracy, precision, recall, and F1-score are selected as the four main evaluation indicators. Moreover, the hyperparameters of each model are determined by the grid search method and the cross-validation method. The experimental results show that after hybrid feature selection, the optimal results of the four main evaluation indicators of the BGD-LR model are 0.9912, 1, 0.9886, and 0.9943, in which the accuracy, recall, and F1-scores are increased by 2.63%, 3.41%, and 1.76%, respectively. For the BDP-LR model, when the privacy budget ε is taken as 0.8, the classification performance and privacy protection effect of the model reach an effective balance. At the same time, the four main evaluation indicators of the model are 0.9721, 0.9975, 0.9664, and 0.9816, which are improved by 1.58%, 0.26%, 1.81%, and 1.07%, respectively. Comparative analysis shows that the models of BGD-LR and BDP-LR constructed in this paper perform better than other classification models.