Differentially-private software frequency profiling under linear constraints

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.

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

Federated learning data protection scheme based on personalized differential privacy in psychological evaluation

Early detection and treatment of disease are paramount for our well-being. Regular clinical visits are essential for patients with chronic illnesses to prevent the disease from progressing, and individuals with high-risk health factors, such as family history of a specific disease, also benefit from frequent health checks. Effective health monitoring relies on two essential factors. First, a timely and robust algorithm is indispensable for identifying early warning signs of disease from complex health indicators. Second, privacy protection during data usage is crucial due to the sensitive nature of clinical data. For instance, a seemingly innocuous indicator (e.g. neutrophils in asthma detection) may be associated with stigmatized illnesses (e.g. HIV/AIDS) that cause social isolation and discrimination. To achieve disease surveillance in a timely and privacy-preserving manner, we develop an online monitoring algorithm based on differential privacy. We incorporate an independent, Laplace-distributed random variable into the statistics of control chart. Further, we introduce an adaptive estimation method and a weighting function to reduce the sensitivity loss in the monitoring and robustness loss in the private monitoring algorithm. The proposed method adheres to the differential data privacy model. Simulation results and a real example of personal clinical diagnosis demonstrate the superiority of our design.

BackgroundHealth physical examinations play a crucial role in early detection of cancer and chronic disease. However, privacy concerns limit the utilization of this kind of data for health interventions and research. Synthetic data methods based on differential privacy are increasingly used to create complete datasets that protect privacy while enabling data analysis and result interpretation. Hence, the use of synthetic algorithms based on differential privacy for privacy protection of physical examination data is a promising research direction.MethodsThree synthetic algorithms, PrivBayes, PeGS, and DP-Gibbs were used to generate complete synthetic datasets that adhere to differential privacy standards using physical examination data composed of categorical data, which compared with the existing algorithm Private-PGM.ResultsCompared with the existing algorithm, DP-Gibbs can provide privacy preserving capacity of 4.686 (ε = 0.5), while the existing algorithm only with 2.012. In addition, DP-Gibbs provides 0.620 of precision, 0.539 of F1-score, 0.342 of Kappa Coefficient, and 0.765 of AUC-score. The corresponding statistical results of existing algorithm are 0.520, 0.321, 0.188 and 0.695.ConclusionsThe main contributions of this study are the exploration of combination models incorporating different noise forms and Bayesian synthetic algorithms, alongside a comparative analysis against existing algorithms. This study explored the balance between privacy protection and data utility under different levels of privacy protection, and DP-Gibbs offers more stable technical support for de-identifying physical examination data prior to sharing and analysis, which realized the mining and application of a wider range of medical data under the requirements of privacy protection. By leveraging this effective privacy protection technique, clinical researchers can extract valuable insights on diseases and population health from the physical examination data without the risk of leaking private information.Supplementary InformationThe online version contains supplementary material available at 10.1186/s12911-025-03109-1.

The existing privacy protection schemes for Location‐Based Service (LBS) only protect users' location privacy or query privacy, which can not adopt both of the privacy protections simultaneously in the LBS system. Moreover, these schemes cannot take into account the spatial‐temporal correlation and background knowledge. In response to the above mentioned questions, the LBS Privacy Protection Scheme Based on Differential Privacy (DPLQ) is proposed. The method contains two kinds of privacy protection algorithms: users' location privacy protection algorithm and users' query privacy protection algorithm. The users' location privacy protection algorithm divides the map using the Voronoi diagram, choosing l fake location points based on the improved k ‐means algorithm and l ‐diversity idea, and protects users' location privacy with the Laplace mechanism. Based on the k ‐anonymous algorithm, the users' query privacy protection algorithm builds a query k ‐anonymous set according to the neighbour users' query requests at the same time t in the cluster and the historical query probability of the region’s POI and protects users' query privacy with the exponential mechanism. Through setting the privacy protection intensity of the algorithm by the users, the generated location dataset and query k ‐anonymous set can resist a variety of attacks from malicious attackers. Theoretical analysis and experimental results show that the scheme can effectively protect the location privacy and query privacy of users.

Implications of Data Anonymization on the Statistical Evidence of Disparity

In the data-driven healthcare sector, balancing privacy protection and model performance is critical. This paper enhances accuracy and reliability in survival analysis by integrating differential privacy, deep learning, and the Cox proportional hazards model within a federated learning framework. Traditionally, differential privacy’s noise injection often degrades model performance. To address this, we propose two adaptive privacy budget allocation strategies considering weight changes across neural network layers. The first, LS-ADP, utilizes layer sensitivity to assess the influence of individual layer weights on model performance and develops an adaptive differential privacy algorithm. The second, ROW-DP, comprehensively assesses weight variations and absolute values to propose a random one-layer weighted differential privacy algorithm. These algorithms provide differentiated privacy protection for various weights, mitigating privacy leakage while ensuring model performance. Experimental results on simulated and clinical datasets demonstrate improved predictive performance and robust privacy protection.

The primary motivation is to address difficulties in data interpretation or a reduction in model accuracy. Although differential privacy can provide data privacy guarantees, it also creates problems. Thus, we need to consider the noise setting for differential privacy is currently inconclusive. This paper’s main contribution is finding a balance between privacy and accuracy. The training data of deep learning models may contain private or sensitive corporate information. These may be dangerous to attacks, leading to privacy data leakage for data sharing. Many strategies are for privacy protection, and differential privacy is the most widely applied one. Google proposed a federated learning technology to solve the problem of data silos in 2016. The technology can share information without exchanging original data and has made significant progress in the medical field. However, there is still the risk of data leakage in federated learning; thus, many models are now used with differential privacy mechanisms to minimize the risk. The data in the financial field are similar to medical data, which contains a substantial amount of personal data. The leakage may cause uncontrollable consequences, making data exchange and sharing difficult. Let us suppose that differential privacy applies to the financial field. Financial institutions can provide customers with higher value and personalized services and automate credit scoring and risk management. Unfortunately, the economic area rarely applies differential privacy and attains no consensus on parameter settings. This study compares data security with non-private and differential privacy financial visual models. The paper finds a balance between privacy protection with model accuracy. The results show that when the privacy loss parameter ϵ is between 12.62 and 5.41, the privacy models can protect training data, and the accuracy does not decrease too much.

Personal big data pricing method based on differential privacy

Mobile crowdsensing (MCS) has heated up and has become a new paradigm of data collection. In the process of the task allocation of MCS, users are often required to provide their own location information with the server to conveniently dispatch some suitable tasks to them. However, it is possible for malicious servers to infer some sensitive information based on the user’s location such as the user’s home address or the user’s trajectory and so on which will cause serious privacy issues. Recently, differential privacy (DP) has become a promising privacy protection scheme. However, the existing DP schemes in location privacy protection for MCS do not pay attention to the accuracy of task allocation as equally as the effects of privacy protection, which often results in the task allocation to be inaccurate and inefficient. In order to overcome the shortcoming, we propose a novel MCS task allocation scheme integrating the mapping accuracy of task-worker with the privacy-preserving effect on the user’s location. Besides edge devices, its implementation system consists of a task allocation server and a third party, both of which are semitrusted, that is, both of them only know about the user’s rough location information but cannot obtain the user’s exact location. At first, to improve the response speed of MCS task requests and the location privacy protection of MCS users, our scheme can exploit more than one edge-computing node nearby a user to cooperatively participate in MCS task allocation by aggregating him/her and the near users into groups. Then, our scheme can further enhance the location privacy protection effect based on the Johnson–Lindenstrauss (JL) transformation, which can achieve accurate task allocation and hold the merits of DP. Finally, we verify the feasibility by some experiments based on two data sets. The performance is compared with that of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$t$ </tex-math></inline-formula> he typical DP. The results show that our scheme not only provides strict privacy guarantees but also has higher performance.

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.

The rapid growth of the internet of medical things (IoMT) in practical life causes privacy concerns for data owners at the time of data analysis. There are many methods available to examine the IoMT data in a privacy-preserving manner but a study is needed to understand the best practical method for real life. So, this study will not propose a novel method but will explore the state-of-the-art method to address the most realistic method for privacy-preserving data analysis. This considered two widely used technologies are Cryptography and Differential privacy. Performances are considered in terms of accuracy and time consumptions for the latest published research. Based on the analysis it is clear that both methods have flaws and can be used depending on the constraints of domains. Moreover, Differential privacy is more practical as it has almost similar time consumptions just like standard unsecured methods.

Data privacy has been an important research topic in the security, theory and database communities in the last few decades. However, many existing studies have restrictive assumptions regarding the adversary's prior knowledge, meaning that they preserve individuals' privacy only when the adversary has rather limited background information about the sensitive data, or only uses certain kinds of attacks. Recently, differential privacy has emerged as a new paradigm for privacy protection with very conservative assumptions about the adversary's prior knowledge. Since its proposal, differential privacy had been gaining attention in many fields of computer science, and is considered among the most promising paradigms for privacy-preserving data publication and analysis. In this tutorial, we will motivate its introduction as a replacement for other paradigms, present the basics of the differential privacy model from a database perspective, describe the state of the art in differential privacy research, explain the limitations and shortcomings of differential privacy, and discuss open problems for future research.

Collaborative filtering recommendation systems generate personalized recommendation results by analyzing and collaboratively processing a large numerous of user ratings or behavior data. The widespread use of recommendation systems in daily decision-making also brings potential risks of privacy leakage. Recent literature predominantly employs differential privacy to achieve privacy protection, however, many schemes struggle to balance user privacy and recommendation performance effectively. In this work, we present a practical privacy-preserving scheme for user-based collaborative filtering recommendation that utilizes fuzzy C-means clustering and Shapley value, FSPPCFs, aiming to enhance the recommendation performance while ensuring privacy protection. Specifically, (i) we have modified the traditional recommendation scheme by introducing a similarity balance factor integrated into the Pearson similarity algorithm, enhancing recommendation system performance; (ii) FSPPCFs first clusters the dataset through fuzzy C-means clustering and Shapley value, grouping users with similar interests and attributes into the same cluster, thereby providing more accurate data support for recommendations. Then, differential privacy is used to achieve the user’s personal privacy protection when selecting the neighbor set from the target cluster. Finally, it is theoretically proved that our scheme satisfies differential privacy. Experimental results illustrate that our scheme significantly outperforms existing methods.