294,335 research outputs found

    On the Differential Privacy of Bayesian Inference

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    We study how to communicate findings of Bayesian inference to third parties, while preserving the strong guarantee of differential privacy. Our main contributions are four different algorithms for private Bayesian inference on proba-bilistic graphical models. These include two mechanisms for adding noise to the Bayesian updates, either directly to the posterior parameters, or to their Fourier transform so as to preserve update consistency. We also utilise a recently introduced posterior sampling mechanism, for which we prove bounds for the specific but general case of discrete Bayesian networks; and we introduce a maximum-a-posteriori private mechanism. Our analysis includes utility and privacy bounds, with a novel focus on the influence of graph structure on privacy. Worked examples and experiments with Bayesian na{\"i}ve Bayes and Bayesian linear regression illustrate the application of our mechanisms.Comment: AAAI 2016, Feb 2016, Phoenix, Arizona, United State

    Crypto'Graph: Leveraging Privacy-Preserving Distributed Link Prediction for Robust Graph Learning

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    Graphs are a widely used data structure for collecting and analyzing relational data. However, when the graph structure is distributed across several parties, its analysis is particularly challenging. In particular, due to the sensitivity of the data each party might want to keep their partial knowledge of the graph private, while still willing to collaborate with the other parties for tasks of mutual benefit, such as data curation or the removal of poisoned data. To address this challenge, we propose Crypto'Graph, an efficient protocol for privacy-preserving link prediction on distributed graphs. More precisely, it allows parties partially sharing a graph with distributed links to infer the likelihood of formation of new links in the future. Through the use of cryptographic primitives, Crypto'Graph is able to compute the likelihood of these new links on the joint network without revealing the structure of the private individual graph of each party, even though they know the number of nodes they have, since they share the same graph but not the same links. Crypto'Graph improves on previous works by enabling the computation of a certain number of similarity metrics without any additional cost. The use of Crypto'Graph is illustrated for defense against graph poisoning attacks, in which it is possible to identify potential adversarial links without compromising the privacy of the graphs of individual parties. The effectiveness of Crypto'Graph in mitigating graph poisoning attacks and achieving high prediction accuracy on a graph neural network node classification task is demonstrated through extensive experimentation on a real-world dataset

    Graph Analysis in Decentralized Online Social Networks with Fine-Grained Privacy Protection

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    Graph analysts cannot directly obtain the global structure in decentralized social networks, and analyzing such a network requires collecting local views of the social graph from individual users. Since the edges between users may reveal sensitive social interactions in the local view, applying differential privacy in the data collection process is often desirable, which provides strong and rigorous privacy guarantees. In practical decentralized social graphs, different edges have different privacy requirements due to the distinct sensitivity levels. However, the existing differentially private analysis of social graphs provide the same protection for all edges. To address this issue, this work proposes a fine-grained privacy notion as well as novel algorithms for private graph analysis. We first design a fine-grained relationship differential privacy (FGR-DP) notion for social graph analysis, which enforces different protections for the edges with distinct privacy requirements. Then, we design algorithms for triangle counting and k-stars counting, respectively, which can accurately estimate subgraph counts given fine-grained protection for social edges. We also analyze upper bounds on the estimation error, including k-stars and triangle counts, and show their superior performance compared with the state-of-the-arts. Finally, we perform extensive experiments on two real social graph datasets and demonstrate that the proposed mechanisms satisfying FGR-DP have better utility than the state-of-the-art mechanisms due to the finer-grained protection

    Differentially Private Decoupled Graph Convolutions for Multigranular Topology Protection

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    Graph learning methods, such as Graph Neural Networks (GNNs) based on graph convolutions, are highly successful in solving real-world learning problems involving graph-structured data. However, graph learning methods expose sensitive user information and interactions not only through their model parameters but also through their model predictions. Consequently, standard Differential Privacy (DP) techniques that merely offer model weight privacy are inadequate. This is especially the case for node predictions that leverage neighboring node attributes directly via graph convolutions that create additional risks of privacy leakage. To address this problem, we introduce Graph Differential Privacy (GDP), a new formal DP framework tailored to graph learning settings that ensures both provably private model parameters and predictions. Furthermore, since there may be different privacy requirements for the node attributes and graph structure, we introduce a novel notion of relaxed node-level data adjacency. This relaxation can be used for establishing guarantees for different degrees of graph topology privacy while maintaining node attribute privacy. Importantly, this relaxation reveals a useful trade-off between utility and topology privacy for graph learning methods. In addition, our analysis of GDP reveals that existing DP-GNNs fail to exploit this trade-off due to the complex interplay between graph topology and attribute data in standard graph convolution designs. To mitigate this problem, we introduce the Differentially Private Decoupled Graph Convolution (DPDGC) model, which benefits from decoupled graph convolution while providing GDP guarantees. Extensive experiments on seven node classification benchmarking datasets demonstrate the superior privacy-utility trade-off of DPDGC over existing DP-GNNs based on standard graph convolution design

    Review Paper-Social networking with protecting sensitive labels in data Anonymization

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    The use of social network sites goes on increasing such as facebook, twitter, linkedin, live journal social network and wiki vote network. By using this, users find that they can obtain more and more useful information such as the user performance, private growth, dispersal of disease etc. It is also important that users private information should not get disclose. Thus, Now a days it is important to protect users privacy and utilization of social network data are challenging. Most of developer developed privacy models such as K-anonymity for protecting node or vertex reidentification in structure information. Users privacy models get forced by other user, if a group of node largely share the same sensitive labels then other users easily find out one’s data ,so that structure anonymization method is not purely protected. There are some previous approaches such as edge editing or node clustering .Here structural information as well as sensitive labels of individuals get considered using K-degree l-deversityanonymity model. The new approach in anonymization methodology is adding noise nodes. By considering the least distortion to graph properties,the development of new algorithm using noise nodes into original graph. Most important it will provide an analysis of no.of noise nodes added and their impact on important graph property

    Structural Mutual Information and Its Application

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    Shannon mutual information is an effective method to analyze the information interaction in a point-to-point communication system. However, it cannot solve the problem of channel capacity in graph structure communication system. This problem make it impossible to use traditional mutual information (TMI) to detect the real information and to measure the information embedded in the graph structure. Therefore, measuring the interaction of graph structure and the degree of privacy leakage has become an emerging and challenging issue to be considered. To solve this issue, we propose a novel structural mutual information (SMI) theory based on structure entropy model and the Shannon mutual information theorem, following by the algorithms for solving SMI. The SMI is used to detect the real network structure and measure the degree of private data leakage in the graph structure. Our work expands the channel capacity of Shannon’s second theorem in graph structure, discusses the correlation properties between SMI and TMI, and concludes that SMI satisfies some basic properties, including symmetry, non-negativity, and so on. Finally, theoretical analysis and example demonstration show that the SMI theory is more effective than the traditional privacy measurement methods to measure the information amount embedded in the graph structure and the overall degree of privacy leakage. It provides feasible theoretical support for the privacy protection technology in the graph structure
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