What is the difference between metric differential privacy and classical differential privacy?
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Metric differential privacy extends beyond classical differential privacy by incorporating quantitative information flow principles, offering a more nuanced approach to privacy-utility trade-offs. While classical differential privacy focuses on concealing individual contributions through noise addition , metric differential privacy introduces the concept of universal optimality for specific queries, aiming to provide better utility while maintaining privacy constraints . This extension allows for a broader evaluation of mechanisms in various application domains, such as location privacy and privacy-preserving machine learning, by considering different loss functions and privacy capacities . In contrast, classical differential privacy primarily emphasizes the balance between accuracy and privacy through noise injection, impacting data analysis accuracy based on the level of added noise .
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| Papers (5) | Insight |
|---|---|
| Metric differential privacy focuses on continuous data while classical differential privacy is for discrete data. Both ensure privacy by adding noise to aggregate findings, preserving individual anonymity in social networks. | |
01 Aug 2022 | Metric differential privacy is a generalization encompassing standard and local differential privacy, crucial in privacy applications. It utilizes quantitative information flow and diverse loss functions for privacy-utility trade-offs. |
| Metric differential privacy introduces additional dummy data to address sensitivity calculation challenges, enhancing privacy guarantees compared to classical differential privacy, as demonstrated in the paper. | |
| Not addressed in the paper. | |
14 Jun 2023 | Not addressed in the paper. |
Related Questions
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What were the primary privacy approaches that led to the development of differential privacy?5 answersThe development of differential privacy was primarily driven by the need to balance privacy and data utility. This approach involves adding noise to datasets to protect individual privacy while still allowing for meaningful analysis. Differential privacy addresses the challenge of aggregating sensitive user data without compromising privacy, making it a widely accepted method for ensuring statistical privacy. The concept involves evaluating the trade-off between accuracy and privacy, with the understanding that too little noise can render analyses practically useless. Additionally, differential privacy leverages mechanisms like Laplace noise addition to safeguard sensitive information effectively. Overall, the fundamental idea behind differential privacy is to provide a framework that allows for valuable data analysis while preserving individual privacy.
How can differential privacy be used to protect sensitive data?4 answersDifferential privacy is a technique used to protect sensitive data by adding noise to the data while still maintaining statistical accuracy. It has been widely used in various fields such as social science, data science, public health, and information technology. Different methods have been proposed to achieve differential privacy, such as distribution-invariant privatization (DIP) and pattern-level differential privacy (DP). These methods aim to reconcile the trade-off between privacy protection and statistical accuracy, ensuring that any downstream analysis or machine learning task yields the same conclusion as if the original data were used. Additionally, task-specific adaptive differential privacy (DP) techniques have been proposed to calibrate the amount and distribution of random noise applied to each attribute based on the feature importance for specific machine learning tasks, resolving the privacy-utility trade-off problem. By incorporating differential privacy techniques, sensitive data, such as fingerprints, can be protected while maintaining robustness against privacy attacks. Furthermore, differential privacy solutions have been proposed to enforce external invariants and integer constraints on data products, ensuring privacy guarantees while maintaining statistical usability.
How can differential privacy be used to protect graph data?5 answersDifferential privacy (DP) can be used to protect graph data by ensuring both model parameter and prediction privacy. Graph Differential Privacy (GDP) is a formal DP framework tailored to graph learning settings that provides provably private model parameters and predictions. To achieve an ideal balance between accuracy and privacy in Graph Neural Networks (GNNs), a progressive training scheme can be used. ProGAP, a differentially private GNN, splits the model into a sequence of overlapping submodels that are trained progressively, leading to improved accuracy-privacy trade-offs. Another approach is to use a Heterogeneous graph neural network with Semantic-aware Differential privacy Guarantees (HeteSDG), which provides double privacy guarantees for both graph features and topology. Additionally, node-level and edge-level perturbation algorithms can be used to publish graphs under node differential privacy, allowing for flexible privacy guarantees.
How can differential privacy be used on graph learning?4 answersDifferential privacy can be used in graph learning to protect the privacy of participants and obtain accurate estimates of global graph properties. One approach is to apply decentralized differential privacy (DDP) to enforce privacy for all participants, considering not only their own privacy but also the privacy of their neighbors involved in their extended local views (ELVs). Another approach is to use local differential privacy (LDP) to ensure privacy while sparsifying graphs and approximating the spectrum of the input graph. Additionally, differential privacy can be applied to learning Markov Random Fields, including structure learning and parameter learning, under various privacy constraints. Privacy-preserving graph neural networks can also be used, where node features are perturbed and compressed under local differential privacy, and a denoising layer is added to improve accuracy. Finally, a privacy-preserving representation learning framework on graphs can be used, incorporating a primary learning task and a privacy protection task, with mutual information objectives.
How can differential privacy be used on graph learning?5 answersDifferential privacy can be used in graph learning to protect the privacy of sensitive data while still allowing for effective analysis. One approach is to use the differential privacy framework to enforce privacy constraints on deep graph generation models. This can be done by injecting noise into the gradients of the model to ensure individual link privacy while preserving data utility. Another approach is to use local differential privacy to perturb and compress node features in a graph neural network. This allows for the central server to collect and approximate the network's neighborhood aggregation step while maintaining privacy guarantees. Additionally, a web-based benchmark platform called DPGraph has been developed to evaluate private algorithms on graph data. This platform allows users to understand the trade-off between privacy, accuracy, and performance of different algorithms for graph statistics.