SoK: Usability Studies in Differential Privacy

Differential Privacy in the 2020 Decennial Census and the Implications for Available Data Products

Recent emerging mobile and wearable technologies make it easy to collect personal spatiotemporal data such as activity trajectories in daily life. Releasing real-time statistics over trajectory streams produced by crowds of people is expected to be valuable for both academia and business, answering questions such as How many people are in Central Station now? However, analyzing these raw data will entail risks of compromising individual privacy. ?-Differential Privacy has emerged as a de facto standard for private statistics publishing because of its guarantee of being rigorous and mathematically provable. Since user trajectories will be generated infinitely, it is difficult to protect every trajectory under ?-differential privacy. To this end, we propose a flexible privacy model of l-trajectory privacy to ensure every length of l trajectories under protection of ?-differential privacy. Then we hierarchically design algorithms to satisfy l-trajectory privacy. Experiments using four real-life datasets show that our proposed algorithms are effective and efficient.

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

The rapid evolution of artificial intelligence (AI) has transformed how personal data is harnessed, driving innovation in diverse fields, from healthcare to finance. However, this transformation brings complex challenges in safeguarding individual privacy while leveraging vast amounts of personal information. This paper examines the dual role of AI as both a catalyst for technological advancement and a potential risk to privacy. By analyzing current trends in AI data usage, exploring the inherent challenges in balancing privacy and functionality, and highlighting emerging solutions—such as federated learning and differential privacy—the study offers a comprehensive overview of strategies for responsible AI development. The findings underscore the need for ethical frameworks, robust regulatory measures, and innovative privacy-preserving techniques to ensure that the benefits of AI do not come at the expense of personal data protection. Index Terms— Artificial Intelligence (AI), Personal Data, Privacy, Data Protection, Privacy-Preserving Machine Learning, Federated Learning, Differential Privacy, Ethical AI, Data Governance, Regulatory Compliance.

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.

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.

This study addresses the challenge of extracting valuable information and selecting key variables from large datasets, essential across statistics, computational science, and data science. In the age of big data, where safeguarding personal privacy is paramount, this study presents an online learning algorithm that leverages differential privacy to handle large‐scale data effectively. The focus is on enhancing the online group lasso approach within the differential privacy realm. The study begins by comparing online and offline learning approaches and classifying common online learning techniques. It proceeds to elucidate the concept of differential privacy and its importance. By enhancing the group‐follow‐the‐proximally‐regularized‐leader (GFTPRL) algorithm, we have created a new method for the online group lasso model that integrates differential privacy for binary classification in logistic regression. The research offers a solid validation of the algorithm’s effectiveness based on differential privacy and online learning principles. The algorithm’s performance was thoroughly evaluated through simulations with both synthetic and actual data. The comparison is made between the proposed privacy‐preserving algorithm and traditional non‐privacy‐preserving counterparts, with a focus on regret bounds, a measure of performance. The findings underscore the practical benefits of the differential privacy‐preserving algorithm in tackling large‐scale data analysis while upholding privacy standards. This research marks a significant step forward in the fusion of big data analytics and the safeguarding of individual privacy.

In this work we investigate the problem of private statistical analysis of time-series data in the distributed and semi-honest setting. In particular, we study some properties of Private Stream Aggregation (PSA), first introduced by Shi et al. 2011. This is a computationally secure protocol for the collection and aggregation of data in a distributed network and has a very small communication cost. In the non-adaptive query model, a secure PSA scheme can be built upon any key-homomorphic weak pseudo-random function as shown by Valovich 2017, yielding security guarantees in the standard model which is in contrast to Shi et al. We show that every mechanism which preserves \((\epsilon ,\delta )\)-differential privacy in effect preserves computational \((\epsilon ,\delta )\)-differential privacy when it is executed through a secure PSA scheme. Furthermore, we introduce a novel perturbation mechanism based on the symmetric Skellam distribution that is suited for preserving differential privacy in the distributed setting, and find that its performances in terms of privacy and accuracy are comparable to those of previous solutions. On the other hand, we leverage its specific properties to construct a computationally efficient prospective post-quantum protocol for differentially private time-series data analysis in the distributed model. The security of this protocol is based on the hardness of a new variant of the Decisional Learning with Errors (DLWE) problem. In this variant the errors are taken from the symmetric Skellam distribution. We show that this new variant is hard based on the hardness of the standard Learning with Errors (LWE) problem where the errors are taken from the discrete Gaussian distribution. Thus, we provide a variant of the LWE problem that is hard based on conjecturally hard lattice problems and uses a discrete error distribution that is similar to the continuous Gaussian distribution in that it is closed under convolution. A consequent feature of the constructed prospective post-quantum protocol is the use of the same noise for security and for differential privacy.

In recent years, differential privacy technology has been introduced into deep learning to satisfy the protection of personal privacy data. However, the existing differential privacy technology will weaken the generalization ability of deep learning networks. To address the problem, a differential privacy noise Bayesian gradient descent algorithm (Dif-NoBa) in Deep Learning was proposed. For the method, the privacy protection algorithm acts on a single neuron in the neural network. Therefore, no matter how complex the network is, it only focuses on a single neuron, so that each neuron can satisfy its own differential privacy, and then the whole network can meet the differential privacy, effectively solving the problem of insufficient generalization ability of neural network caused by adding arbitrary noise.

Data analysis is expected to provide accurate descriptions of the data. However, this is in opposition to privacy requirements when working with sensitive data. In this case, there is a need to ensure that no disclosure of sensitive information takes place by releasing the data analysis results. Therefore, privacy-preserving data analysis has become significant. Enforcing strict privacy guarantees can significantly distort data or the results of the data analysis, thus limiting their analytical utility (i.e., differential privacy). In an attempt to address this issue, in this paper we discuss how “integral privacy”; a re-sampling based privacy model; can be used to compute descriptive statistics of a given dataset with high utility. In integral privacy, privacy is achieved through the notion of stability, which leads to release of the least susceptible data analysis result towards the changes in the input dataset. Here, stability is explained by the relative frequency of different generators (re-samples of data) that lead to the same data analysis results. In this work, we compare the results of integrally private statistics with respect to different theoretical data distributions and real world data with differing parameters. Moreover, the results are compared with statistics obtained through differential privacy. Finally, through empirical analysis, it is shown that the integral privacy based approach has high utility and robustness compared to differential privacy. Due to the computational complexity of the method we propose that integral privacy to be more suitable towards small datasets where differential privacy performs poorly. However, adopting an efficient re-sampling mechanism can further improve the computational efficiency in terms of integral privacy.

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.

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.

Personal big data pricing method based on differential privacy

With recent developments in communication and data storage technologies, the Internet is gathering and storing an explosive amount of information. While such a large volume of data provides great opportunities for the exploration of knowledge, organizations do not want to share their information for legal or competitive reasons. This raises the problem of information mining while retaining privacy. Current efficient data mining privacy preservation algorithms are focused on the assumption that all intermediate results during data mining operations are appropriate to be published. It has been shown, however, that such intermediate outcomes can still leak private information. Differential privacy is used in this situation to quantitatively restrict such leakage of information. Differential privacy is a newly emerged concept of privacy that can provide strong observable assurances of privacy. The rapid growth of the Internet of Things has contributed to the pervasive deployment of low-cost smart devices and the widespread use of high-speed wireless networks (IoT). IoT welcomes numerous physical artifacts that have not been active in the conventional Internet and allows a wide variety of IoT applications to be provided through their interaction and cooperation. Many IoT services which require a detailed understanding and analysis of data collected across a large number of physical devices that challenge both the privacy of personal information and IoT creation. IoT information privacy is a broad and complex term since its understanding and interpretation vary between individuals and involves efforts from both legislation and technology to implement it. In this paper we review IoT privacy enhancement using different privacy strategies on various IoT applications and challenges with Security & Privacy. Keywords : Internet of Things, Differential Privacy, Security, Privacy, Data Mining, Data Set, Server.

Differential privacy (DP) is the state-of-the-art and rigorous notion of privacy for answering aggregate database queries while preserving the privacy of sensitive information in the data. In today’s era of data analysis, however, it poses new challenges for users to understand the trends and anomalies observed in the query results: Is the unexpected answer due to the data itself, or is it due to the extra noise that must be added to preserve DP? In the second case, even the observation made by the users on query results may be wrong. In the first case, can we still mine interesting explanations from the sensitive data while protecting its privacy? To address these challenges, we present a three-phase framework DPXPlain, which is the first system to the best of our knowledge for explaining group-by aggregate query answers with DP. In its three phases, DPXPlain (a) answers a group-by aggregate query with DP, (b) allows users to compare aggregate values of two groups and with high probability assesses whether this comparison holds or is flipped by the DP noise, and (c) eventually provides an explanation table containing the approximately ‘top-k’ explanation predicates along with their relative influences and ranks in the form of confidence intervals, while guaranteeing DP in all steps. We perform an extensive experimental analysis of DPXPlain with multiple use-cases on real and synthetic data showing that DPXPlain efficiently provides insightful explanations with good accuracy and utility.