Cycle-to-cycle Variation Enabled Energy Efficient Privacy Preserving Technology in ANN

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.

In response to the privacy leakage risks inherent in the big data processing of power user personas, propose a collaborative optimization‐based joint privacy protection mechanism for Differential Privacy (DP) and Secret Sharing (SS). This method reveals the inherent correlation between DP noise parameters and SS sharding strategy and designs an adaptive noise sharding mapping mechanism to achieve joint optimization of privacy protection and communication overhead. Based on this mechanism, a specific privacy protection scheme for power user personas is designed. Experimental results demonstrate that the DP + SS privacy protection method designed in this study achieves an accuracy rate of 93.14% on the MNIST dataset and maintains an accuracy rate of 92.06% across multiple datasets, indicating that the scheme effectively quantifies the level of privacy protection. The privacy protection level of the scheme is quantitatively defined through differential privacy budget ε ∈ [0.11,0.15], secret sharing ( k = 3, n = 5) threshold mechanism, and privacy leakage probability δ = 10 −5 . The privacy budget is dynamically allocated through the correlation between differential privacy noise variance and the minimum reconstruction threshold of secret sharing, ensuring that privacy risks are controllable and quantifiable. At the same time, the average training time for servers is about 186.4 s, which refers to the total end‐to‐end training time in a four‐server cluster environment. Compared with a single server encryption scheme, the efficiency is improved by 48.6%. The communication overhead has increased to 10.6GB, which is the total chip transmission overhead per 10,000 users. However, the calculation stability can be maintained through load balancing, indicating that the scheme has certain feasibility. The aforementioned research results not only provide a viable applied cryptographic technology solution for privacy protection in power big data but also offer new insights into balancing privacy protection strength and data availability. More importantly, the joint privacy protection mechanism proposed by the research institute can serve as a fundamental enabling technology for the infrastructure of a secure smart grid, providing quantifiable and trustworthy privacy and security guarantees for advanced data‐driven applications such as precise load prediction, demand‐side response, and collaborative optimization of distributed energy, thereby supporting the reliable and intelligent development of the smart grid.

Decentralized IoT data sharing: A blockchain-based federated learning approach with joint optimizations for efficiency and privacy

OPTDP: Towards optimal personalized trajectory differential privacy for trajectory data publishing

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.

With the rapid development of the Internet, privacy protection in big data has become an important research hotspot in the field of information security. Among them, differential privacy protection is the most widely used big data privacy protection technology today. However, the existing differential privacy protection technology cannot fully and effectively deal with the problem of high-dimensional privacy big data. Although the private data publishing method based on a Bayesian network can effectively deal with the conversion problem of high-dimensional data sets to low-dimensional data sets, this method also has some defects and deficiencies. Therefore, this paper proposes a large data source differential privacy protection algorithm (NIE-PrivBayes algorithm) based on normalized information entropy Bayesian network and proposes three improvements to the original PrivBayes algorithm. First, the original data set is protected by differential privacy on the client side. Secondly, the Bayesian network is constructed by the joint probability distribution obtained by the EM algorithm. Finally, the existing Bayesian network construction algorithm is optimized in the aspects of normalized information entropy. Based on significantly reducing the computational overhead, the effectiveness and availability of publishing high-dimensional data sets are realized under the premise of differential privacy protection.

As a new type of distributed machine learning technology, federated learning has shown great application potential in the Internet of things, health care, smart home, finance and other fields. Its core advantage is that it can conduct model training without centralized data, effectively reducing the cost of data transmission and storage, and avoiding the risk of privacy disclosure. However, with the wide application of Federated learning, the problems of data security and privacy protection are increasingly apparent, especially in the face of complex network attacks and data leakage risks. This paper deeply analyzes the basic principles and architecture of Federated learning, and discusses the possible privacy threats in data transmission, model updating and participating devices in detail. Combined with the existing security protection technologies, such as differential privacy, encryption algorithm and secure multi-party computing, this paper discusses how to effectively ensure the security of Federated learning. Finally, the article also looks forward to the future development trend of Federated learning in privacy protection, model optimization, computational efficiency and cross domain collaboration, aiming to provide theoretical support and practical guidance for the further development and application of this technology.

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.

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.

Differentially private graph neural networks for graph classification and its adaptive optimization

Face images, as an information carrier, are rich in sensitive information. Direct publication of these images would cause privacy leak, due to their natural weak privacy. Most of the existing privacy protection methods for face images adopt data publication under a non-interactive framework. However, the E-effect under this framework covers the entire image, such that the noise influence is uniform across the image. To solve the problem, this paper proposes region growing publication (RGP), an algorithm for the interactive publication of face images under differential privacy. This innovative algorithm combines the region growing technique with differential privacy technique. The privacy budget E is dynamically allocated, and the Laplace noise is added, according to the similarity between adjacent sub-images. To measure this similarity more effectively, the fusion similarity measurement mechanism (FSMM) was designed, which better adapts to the intrinsic attributes of images. Different from traditional region growing rules, the FSMM fully considers various attributes of images, including brightness, contrast, structure, color, texture, and spatial distribution. To further enhance algorithm feasibility, RGP was extended to atypical region growing publication (ARGP). While RGP limits the region growing direction between adjacent sub-images, ARGP searches for the qualified sub-images across the image, with the aid of the exponential mechanism, thereby expanding the region merging scope of the seed point. The results show that our algorithm can satisfy E-differential privacy, and the denoised image still have a high availability.

Differential privacy is often used in location privacy protection because of its strict reasoning and proof privacy guarantee. When users make continuous location query, it will cause noise superposition, which leads to the decline of query accuracy. At present, although differential privacy based on rule tree structure can reduce the query error, it will generate a lot of invalid zero nodes. The data structure is too large, and more improvement in the query accuracy can be further investigated. In this paper, we proposed a differential privacy location privacy protection method based on generative adversary network. Firstly, the definition of location data privacy protection under differential privacy mechanism is given, and then resume density aware network under differential privacy mechanism. Based on density aware network, the privacy protection problem of location data can be transformed into the distribution of fitting trajectory length. Finally, we use Markov chain to generate a new trajectory, and introduce the generative adversarial network to construct a set differential privacy protection method. Compared with other methods to improve the accuracy of differential privacy query, this method can effectively reduce the problem of query accuracy decline caused by noise superposition in continuous query, and can adapt to lbs location query service in different density environments.KeywordsGenerative adversarial networkLocation differencePrivacy protectionMarkov chainDensity aware network

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.

In this paper, we focus on the privacy preserving mechanism design for crowdsourced Federated Learning (FL), where a requester can outsource its model training task to some workers via an FL platform. A potential way to preserve the privacy of workers' local data is to leverage Differential Privacy (DP) mechanisms on local models. However, most of these studies cannot allow workers to dominate their own privacy protection levels by themselves. Thus, we propose a Personalized Privacy Preserving Mechanism, called P3M, to satisfy the heterogeneous privacy needs of workers, which consists of two parts. The first part includes a personalized privacy budget determination problem. We model it as a two-stage Stackelberg game, derive the personalized privacy budget for each worker and the optimal payment for the requester, and prove that they form a unique Stackelberg equilibrium. Second, we design a dynamic perturbation scheme to perturb model parameters. Through the theoretical analysis, we prove that P3M satisfies the desired DP property, and derive the bounds of the variance of average perturbed parameters and the convergence upper bound. This demonstrates that the global model accuracy can be controllable and P3M is endowed with the satisfactory convergence performance. In addition, we extend our problem to the scenario where the total privacy budget of all workers is limited, so as to prevent some workers from setting exorbitant privacy budgets. Under the privacy constraint, we re-determine the personalized privacy budget for each worker. Finally, exhaustive simulations of P3M are conducted based on real-world datasets, and the experimental results corroborate its effectiveness and practicability.

Differential privacy can effectively help federated learning resist privacy attacks from various parties. However, existing approaches that use differential privacy for privacy protection greatly decrease the model performance of federated learning, especially in scenarios with complex model structures and large parameters. In this paper, we propose a novel privacy preservation scheme for federated learning that combines automatic gradient clipping and gradient transformation perturbation. Our approach primarily reduces the impact of differential privacy on federated learning from two aspects. Firstly, we efficiently control the gradient sensitivity by using automatic gradient clipping instead of traditional threshold clipping. Secondly, we utilize the space transformation technique to alleviate the dramatic accuracy degradation of the model caused by the insertion noise. Extensive experiments on various benchmark datasets demonstrate that our approach achieves a good trade-off between data privacy and effectiveness under the same privacy budget.