Differential Privacy Information Publishing Algorithm based on Cluster Anonymity

Graph data analysis, particularly local triangle counting, plays a pivotal role in deciphering complex relationships within graph data. This method is invaluable across diverse fields such as social networks, transportation, and cybersecurity. However, this process often involves handling sensitive information, necessitating that the relationship between any two nodes is considered private. Differential privacy (DP) is a formal model to address privacy concerns and can be categorized into two types: the central DP (CDP) model, which achieves better result accuracy, and the local DP (LDP) model, which does not assume a trusted server. To bridge the gap between the two models, we propose Sectric, a server-aided crypto-assisted local triangle counting protocol, in this paper. It can achieve the same result accuracy with the same privacy budget as the CDP model without assuming a trusted server. Sectric also explores a new approach in crypto-assisted graph data analysis algorithms that represents a node's neighbors using a set instead of an adjacency vector, and successfully achieves higher efficiency compared to other crypto-assisted solutions. We also conduct theoretical and empirical evaluations to demonstrate that Sectric achieves the design principles.

Differential privacy (DP) is introduced into many fields of AI to preserve privacy. However, introducing DP into AI models is extremely error-prone. To verify whether DP AI models can provide privacy guarantee (quantified by privacy budget) as these models claim, existing methods utilize attack methods to audit whether privacy budget of these models is the same as these models claim. To further improve precision of privacy budget auditing, we propose a brand new way to audit privacy budget, namely directly utilizing the parameters of DP AI models to audit privacy budget. In particular, our method utilizes statistical characteristics variance of the output distribution of DP mechanism to audit privacy budget of DP mechanism. DP AI models are regarded as data samples from output distribution of DP AI model training method and are utilized to approximate the variance of output distribution. The approximated variance is leveraged to estimate the variance of noise distribution of DP mechanism and through the relationship between noise variance and privacy budget, our method calculates the audited privacy budget through estimated noise variance. In addition, to reduce computation overhead, our method constructs parameter selection strategy to identify position whose parameter is suitable for privacy budget auditing. Comprehensive experiments are conducted to verify the effectiveness of our auditing method. Comparison results of five competitive auditing methods demonstrate that our method decreases MAE by 18.29% and decreases MSE by 23.17% on experiment datasets.

Federated learning is a distributed machine learning technique that allows multiple devices to collaborate on learning a shared model without exchanging data. It can be used to improve model accuracy while protecting user privacy. However, traditional federated learning is vulnerable to attacks from generative adversarial networks (GANs). As a new privacy protection method, differential privacy enhances privacy protection capabilities by sacrificing some data accuracy. To optimize the privacy budget allocation scheme in traditional differential privacy, we propose a differential privacy method called ADP-FL, which dynamically adjusts the privacy budget based on Newton’s Law of Cooling. While maintaining the overall privacy budget, it dynamically tunes adaptive parameters to improve training accuracy. Additionally, we propose an asynchronous federated learning aggregation scheme that combines privacy budget with data freshness, thereby reducing the impact of differential privacy on accuracy. We conducted extensive experiments on differential privacy algorithms based on Gaussian mechanisms and Laplace mechanisms. The experimental results show that, under the same privacy budget, our algorithm achieves higher accuracy and lower communication overhead compared to the baseline algorithm.

Privacy budget management plays an important role when applying differential privacy, as it sets an upper limit in the ability to utilise the private database. In this paper, we explore the possibility of extending the total allocated privacy budget, taking into consideration the data consumer characteristics and the data utilisation context. To this end, we first study the problem of privacy budget distribution in adaptive multi-data consumer differential privacy use cases. Then, we present an extension of the classic differential privacy formal model that allows taking into consideration data consumers’ information disclosure risk when distributing the privacy budget among them. Finally, we define a method that allows to optimally distribute a given privacy budget among a private database’s data consumers.

Personal data privacy is a great concern by governments across the world as citizens generate huge amount of data continuously and industries using this for betterment of user centric services. There must be a reasonable balance between data privacy and utility of data. Differential privacy is a promise by data collector to the customer’s personal privacy. Centralised Differential Privacy (CDP) is performing output perturbation of user’s data by applying required privacy budget. This promises the inclusion or exclusion of individual’s data in data set not going to create significant change for a statistical query output and it offers -Differential privacy guarantee. CDP is holding a strong belief on trusted data collector and applying global sensitivity of the data. Local Differential Privacy (LDP) helps user to locally perturb his data and there by guaranteeing privacy even with untrusted data collector. Many differential privacy algorithms handles parameters like privacy budget, sensitivity and data utility in different ways and mostly trying to keep trade-off between privacy and utility of data. This paper evaluates differential privacy algorithms in regard to the privacy support it offers according to the sensitivity of the data. Generalized application of privacy budget is found ineffective in comparison to the sensitivity based usage of privacy budget.

Differential privacy is a precise mathematical constraint meant to ensure privacy of individual pieces of information in a database even while queries are being answered about the aggregate. Intuitively, one must come to terms with what differential privacy does and does not guarantee. For example, the definition prevents a strong adversary who knows all but one entry in the database from further inferring about the last one. This strong adversary assumption can be overlooked, resulting in misinterpretation of the privacy guarantee of differential privacy. Herein we give an equivalent definition of privacy using mutual information that makes plain some of the subtleties of differential privacy. The mutual-information differential privacy is in fact sandwiched between $\epsilon$-differential privacy and $(\epsilon,\delta)$-differential privacy in terms of its strength. In contrast to previous works using unconditional mutual information, differential privacy is fundamentally related to conditional mutual information, accompanied by a maximization over the database distribution. The conceptual advantage of using mutual information, aside from yielding a simpler and more intuitive definition of differential privacy, is that its properties are well understood. Several properties of differential privacy are easily verified for the mutual information alternative, such as composition theorems.

With the continuous development of artificial intelligence technology, machine learning in distributed network systems, such as IoVflntemet of Vehicles), will inevitably lead to privacy leakage. At present, there are lots of problems in federated learning, differential privacy and other machine learning privacy protection schemes, such as high loss of availability and the need of a fully trusted third-party server. In order to solve these problems, we propose MDPFL(Multiple Differential Privacy based on Federated Learning) algorithm. The algorithm combines with the differential privacy model in each stage of federated learning to solve the problem of curious third-party data collectors obtaining users’ original data in the process of machine learning. Meanwhile, the algorithm does not adopt a decentralized machine learning scheme directly, but uses a double noise adding mode with the existence of the third-party data collectors. We use Laplacian mechanism in the central server and GRR mechanism in local clients to ensure the goal of stability of the effect of the machine ¡earning model. The algorithm is compared with FedAvg, CDP, LDP algorithm in accuracy and loss rate which based on EMNIST data sets. In different global sensitivities, the model training effect is consistent with FedAvg algorithm while comparing with LDP algorithm, the speed of model convergence is improved.

Continuous publication of statistics collected from various location-based applications may compromise users’ privacy as the statistics could be procured from users’ private data. Differential Privacy (DP) is a new privacy notion that offers a strong privacy guarantee to all users who participate in the statistics. However, the existing DP mechanism for continuous publication of location statistics provides a privacy guarantee with the assumption that the data-points of users stream at consecutive timestamps are independent. In reality, users’ data-points may be temporally correlated, resulting in more privacy leakage due to an inadequate supply of privacy budget to the timestamps where the data-points are correlated. In this paper, we present a reformulated differential privacy definition to quantify the impact of temporal correlation on privacy leakage. Then, we introduce a privacy budget allocation method for allocating an adequate amount of privacy budget to each successive timestamps under the protection of differential privacy. Our solution adopts w-event privacy for continuously releasing statistics over infinite streams. The main idea is to check the dissimilarity between statistics at each timestamp and decide whether to publish current statistics or last release statistics. Finally, we evaluate the data utility of our proposed method by presenting experimental results for real and synthetic data sets.

Differential privacy is a statistical definition of privacy that has attracted the interest of both academia and industry. Its formulations are easy to understand, but the differential privacy of databases is complicated to determine. One of the reasons for this is that small changes in database programs can break their differential privacy. Therefore, formal verification of differential privacy has been studied for over a decade. In this paper, we propose an Isabelle/HOL library for formalizing differential privacy in a general setting. To our knowledge, it is the first formalization of differential privacy that supports continuous probability distributions. First, we formalize the standard definition of differential privacy and its basic properties. Second, we formalize the Laplace mechanism and its differential privacy. Finally, we formalize the differential privacy of the report noisy max mechanism.

Convolutional neural networks (CNNs) are widely used in the field of image classification. At the same time, users face the risk of privacy leakage because adversaries can reverse private information from the training parameters of CNNs. Adding Gaussian noise to the training parameters is an effective means to prevent adversaries from stealing private, but this tends to reduce the utility of the models. Therefore, how to find a balance between privacy and utility has become a hot research topic. In this paper, to improve the image classification ability of CNN models under differential privacy protection, we propose an image classification algorithm based on layered gradient clipping under differential privacy, ICGC-DP for short. Firstly, the gradient tensor is layered according to the neural network model. Secondly, for each layered gradient tensor, the median of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${L_{2}}$ </tex-math></inline-formula> norms is used as the clipping threshold. Moreover, to prevent the sensitivity from converging to zero, we add a bound on the sensitivity to ensure that all gradients can be protected by differential privacy. To further improve the classification utility of ICGC-DP, we design an adaptive weighted fusion module for it. The module assigns weights to prediction tensors according to the variance between them. We conduct comprehensive experiments on the Mnist, FashionMnist and CIFAR10 datasets, respectively. The experimental results show that, when the privacy budget <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\varepsilon = 2.0$ </tex-math></inline-formula> , which indicates that the algorithm adds a large noise, ICGC-DP achieves 97.36%, 88.72% and 72.63% classification accuracy for the Minist, FasionMnist and CIFAR10 datasets, respectively; when the privacy budget <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\varepsilon = 8.0$ </tex-math></inline-formula> , which means the algorithm adds less noise, the classification accuracy of ICGC-DP for Minist, FasionMnist and CIFAR10 datasets reaches 97.81%, 89.49% and 74.41%, respectively.

Due to the high volume of data available with social networking sites and companies, the privacy of the individuals is at a continuous risk. With the help of auxiliary data, the users can be tracked back. It becomes even more necessary to analyse the huge piles of data for research intentions. Hence, protection of privacy is a big concern. To deal with the privacy concerns, numerous privacy paradigms have been proposed to achieve an equilibrium between data utility and privacy. Anonymization of data before making it public for research is very important. Different privacy models include k-anonymity, l-diversity, t-closeness and differential privacy. This paper explores the role of quasi-identifiers and their roles for anonymization using differential privacy model. The research in this field can pave new ways for thinking before selecting quasi-identifiers for anonymization.

The proliferation of smart devices in recent years has led to novel smart home applications that upgrade traditional home appliances to intelligent units and automatically adapt their services without human assistance. In a smart home system, a central gateway is required to coordinate the functions of various smart home devices and allow bidirectional communications. However, the gateway may cause leakage of sensitive information unless proper privacy protections are applied. In this work, we first introduce a smart home model based on fog computing and secured by differential privacy. Then, we apply a personalized differential privacy scheme to provide privacy protection. Furthermore, we consider a collusion attack and propose our differential privacy model called APDP based on a modified Laplace mechanism and a Markov process to strengthen privacy protection, thus resisting the attack. Lastly, we perform extensive experiments based on the real-world datasets to evaluate the proposed APDP model.

As increasing amounts of sensitive personal information is aggregated into data repositories, it has become important to develop mechanisms for processing the data without revealing information about individual data instances. The differential privacy model provides a framework for the development and theoretical analysis of such mechanisms. In this paper, we propose an algorithm for learning a discriminatively trained multiclass Gaussian mixture model-based classifier that preserves differential privacy using a large margin loss function with a perturbed regularization term. We present a theoretical upper bound on the excess risk of the classifier introduced by the perturbation.

The Stochastic gradient descent algorithm (SGD) is a classical algorithm for model optimization in machine learning. Introducing a differential privacy model to avoid privacy leakages in the optimization iteration process can achieve the balance of training accuracy and data availability. In addition, a fixed number of iterations are chosen in a conventional implementation scheme. At each iteration, parameters are updated with a noisy gradient. However, the privacy budget is mostly split evenly to each iteration without taking into account the difference in the privacy leakage risk under optimal processing. In this paper, we improve the SGD-based algorithms by appropriately allocating the privacy budget for each iteration. Intuitively, the gradient value is inversely proportional to the number of iterations. The closer the parameter is to its optimal objective value, the smaller the gradient is, and hence the gradients need to be measured more accurately. We propose an adaptive “noise reduction” algorithm that can be applied to private SGD-based empirical risk minimization (ERM) algorithms, meets the accuracy constraint simultaneously. We apply our approach to the backpropagation (BP) neural network. In the experiment, we show and validate that the proposed noise parameter configuration method provides sufficient privacy protection and improves the accuracy of data utility.

Sharing behavioral health and wearable data poses privacy challenges, as traditional de-identification remains vulnerable to re-identification. Differential privacy (DP) provides mathematical guarantees through a tunable privacy budget, . This study evaluates the feasibility of generating and releasing DP synthetic behavioral health data with high analytical utility, identifying practical values for public data sharing. We analyzed physiological data from wearable devices and self-reported data from Phase 1 of the Lived Experiences Measured Using Rings Study (LEMURS), which tracked sleep, stress, and well-being among first-year college students. Three DP synthetic data generators: AIM, MST, and PATECTGAN, were evaluated across privacy budgets ranging from to 100. Utility was assessed using L1/L2 errors, correlation, regression, UMAP, and assessed vulnerability via privacy attacks. AIM outperformed MST and PATECTGAN in preserving both statistical and analytical properties of the original data. For the Survey dataset, the lowest marginal errors occurred at and 10. Correlation, regression, and UMAP analyses confirmed that AIM-generated data closely replicated original relationships at moderate values. Choice of privacy budget is still an open question, and it is task-agnostic and dataset-specific. Moderate privacy budgets ( ) maintained key associations between physiological and psychological measures while ensuring privacy. AIM's workload-aware design effectively allocated noise toward relevant features, enhancing performance. A privacy budget of offers a practical balance between data utility and participant privacy for LEMURS behavioral health data sharing.