Differentially Private Real-Time Data Release over Infinite Trajectory Streams

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.

Private record linkage (PRL) is the problem of identifying pairs of records that are similar as per an input matching rule from databases held by two parties that do not trust one another. We identify three key desiderata that a PRL solution must ensure: (1) perfect precision and high recall of matching pairs, (2) a proof of end-to-end privacy, and (3) communication and computational costs that scale subquadratically in the number of input records. We show that all of the existing solutions for PRL? including secure 2-party computation (S2PC), and their variants that use non-private or differentially private (DP) blocking to ensure subquadratic cost -- violate at least one of the three desiderata. In particular, S2PC techniques guarantee end-to-end privacy but have either low recall or quadratic cost. In contrast, no end-to-end privacy guarantee has been formalized for solutions that achieve subquadratic cost. This is true even for solutions that compose DP and S2PC: DP does not permit the release of any exact information about the databases, while S2PC algorithms for PRL allow the release of matching records. In light of this deficiency, we propose a novel privacy model, called output constrained differential privacy, that shares the strong privacy protection of DP, but allows for the truthful release of the output of a certain function applied to the data. We apply this to PRL, and show that protocols satisfying this privacy model permit the disclosure of the true matching records, but their execution is insensitive to the presence or absence of a single non-matching record. We find that prior work that combine DP and S2PC techniques even fail to satisfy this end-to-end privacy model. Hence, we develop novel protocols that provably achieve this end-to-end privacy guarantee, together with the other two desiderata of PRL. Our empirical evaluation also shows that our protocols obtain high recall, scale near linearly in the size of the input databases and the output set of matching pairs, and have communication and computational costs that are at least 2 orders of magnitude smaller than S2PC baselines.

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

The problem of preserving privacy while mining data has been studied extensively in recent years because of its importance for enabling sharing data sets. Differential Identifiability, parameterized by the probability of individual identification \(\rho \), was proposed to provide a solution to this problem. Our study of the proposed Differential Identifiability model shows that: First, its usability is based on a very strong requirement. That is, the prior probability of an individual being present in a database is the same for all individuals. Second, there is no formal link between the proposed model and well known privacy models such as Differential Privacy. This paper presents a new differential identifiability model for preventing the disclosure of the presence of an individual in a database while considering an adversary with arbitrary prior knowledge about each individual. We show that the general Laplace noise addition mechanism can be used to satisfy our new differential identifiability definition and that there is a direct link between differential privacy and our proposed model. The evaluation of our model shows that it provides a good privacy/utility trade-off for most aggregate queries.

Differential privacy is a privacy model that has become quite popular because of the strong privacy guarantees it provides. Differential privacy was initially stated as a privacy guarantee in an interactive setting, where queries are submitted to a database containing the original individual records. However, it is general enough to deal with microdata releases. The principle underlying differential privacy is that the presence or absence of any single individual record in the database or data set should be unnoticeable when looking at the responses returned for the queries.

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.

With the development of Internet of things, more and more personal information needs to be collected in the process of applying Internet of things technology, which brings about the problem of data privacy protection. The algorithm proposed in this paper uses the differential privacy model to process the information network collected by the wireless sensor network terminal in the perception layer of the Internet of things and transmits it to the network layer to achieve the purpose of information protection. In order to reduce the added differential privacy noise, this paper firstly makes dynamic community discovery of the network graph, and adds more noise to the edge of nodes within the community. Then the hierarchical random graph model is used to represent the network graph, and the noise is added to the connection probability between the nodes in the graph, instead of adding noise to the edge directly. Considering the dynamic change of the information collected by the wireless sensor network, this paper proposes a time window partition and dynamic network community discovery algorithm, and publishes a purification map for each time window. Through hierarchical sampling, the time cost is reduced and the cumulative error is reduced, while the network structure characteristics of the time window are maintained as far as possible. Theoretical analysis and experiments show that the algorithm proposed in this paper can preserve the important network structure characteristics of the original graph under the premise of satisfying the differential privacy protection model.

Equivalent mechanism: Releasing location data with errors through differential privacy

This article explores the challenges in data privacy within the big data era with specific focus on differential privacy of social media data and its geospatial realization within a Cloud-based research environment. By using differential privacy method, this paper achieves the distortion of the data by adding noise to protect data privacy. Furthermore, this article presents the IDP k-means Aggregation Optimizing Method to decrease the overlap and superposition of massive data visualization. Finally this paper combines IDP k-means Aggregation Optimizing Method with differential privacy method to protect data privacy. The outcome of this research is a set of underpinning formal models of differential privacy that reflect the geospatial tools challenges faced with location-based information, and the implementation of a suite of Cloud-based tools illustrating how these tools support an extensive range of data privacy demands.

Differential Privacy (DP) has emerged as a pivotal approach for safeguarding individual privacy in data analysis, yet its practical adoption is often hindered by challenges in the implementation and communication of DP. This paper presents a comprehensive systematization of existing research studies around the usability of DP, synthesizing insights from studies on both the practical use of DP tools and strategies for conveying DP parameters that determine privacy protection levels, such as epsilon. By reviewing and analyzing these studies, we identify core usability challenges, best practices, and critical gaps in current DP tools that affect adoption across diverse user groups, including developers, data analysts, and non-technical stakeholders. Our analysis highlights actionable insights and pathways for future research that emphasizes user-centered design and clear communication, fostering the development of more accessible DP tools that meet practical needs and support broader adoption.

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.

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.

A verifiable scheme for differential privacy based on zero-knowledge proofs

Network data often contain sensitive relational information. One approach to protecting sensitive information while offering flexibility for network analysis is to share synthesized networks based on the information in originally observed networks. We employ differential privacy (DP) and exponential random graph models (ERGMs) and propose the DP-ERGM method to synthesize network data. We apply DP-ERGM to two real-world networks. We then compare the utility of synthesized networks generated by DP-ERGM, the DyadWise Randomized Response (DWRR) approach, and the Synthesis through Conditional distribution of Edge given nodal Attribute (SCEA) approach. In general, the results suggest that DP-ERGM preserves the original information significantly better than two other approaches in network structural statistics and inference for ERGMs and latent space models. Furthermore, DP-ERGM satisfies node DP through modeling the global network structure with ERGM, a stronger notion of privacy than the edge DP under which DWRR and SCEA operate.

Privacy preserving data releasing is an important problem for reconciling data openness with individual privacy.The state-of-the-art approach for privacy preserving data release is differential privacy, which offers powerful privacy guarantee without confining assumptions about the background knowledge about attackers.For genomic data with huge-dimensional attributes, however, current approaches based on differential privacy are not effective to handle.Specifically, amount of noise is required to be injected to genomic data with tens of million of SNPs (Single Nucleotide Polymorphisms), which would significantly degrade the utility of released data.To address this problem, this paper proposes a differential privacy guaranteed genomic data releasing method.Through executing belief propagation on factor graph, our method can factorize the distribution of sensitive genomic data into a set of local distributions.After injecting differential-privacy noise to these local distributions, synthetic sensitive data can be obtained by sampling on noise distribution.Synthetic sensitive data and factor graph can be further used to construct approximate distribution of non-sensitive data.Finally, non-sensitive genomic data is sampled from the approximate distribution to construct a synthetic genomic dataset.