3,353 research outputs found
Detecting Communities under Differential Privacy
Complex networks usually expose community structure with groups of nodes
sharing many links with the other nodes in the same group and relatively few
with the nodes of the rest. This feature captures valuable information about
the organization and even the evolution of the network. Over the last decade, a
great number of algorithms for community detection have been proposed to deal
with the increasingly complex networks. However, the problem of doing this in a
private manner is rarely considered. In this paper, we solve this problem under
differential privacy, a prominent privacy concept for releasing private data.
We analyze the major challenges behind the problem and propose several schemes
to tackle them from two perspectives: input perturbation and algorithm
perturbation. We choose Louvain method as the back-end community detection for
input perturbation schemes and propose the method LouvainDP which runs Louvain
algorithm on a noisy super-graph. For algorithm perturbation, we design
ModDivisive using exponential mechanism with the modularity as the score. We
have thoroughly evaluated our techniques on real graphs of different sizes and
verified their outperformance over the state-of-the-art
Private Graph Data Release: A Survey
The application of graph analytics to various domains have yielded tremendous
societal and economical benefits in recent years. However, the increasingly
widespread adoption of graph analytics comes with a commensurate increase in
the need to protect private information in graph databases, especially in light
of the many privacy breaches in real-world graph data that was supposed to
preserve sensitive information. This paper provides a comprehensive survey of
private graph data release algorithms that seek to achieve the fine balance
between privacy and utility, with a specific focus on provably private
mechanisms. Many of these mechanisms fall under natural extensions of the
Differential Privacy framework to graph data, but we also investigate more
general privacy formulations like Pufferfish Privacy that can deal with the
limitations of Differential Privacy. A wide-ranging survey of the applications
of private graph data release mechanisms to social networks, finance, supply
chain, health and energy is also provided. This survey paper and the taxonomy
it provides should benefit practitioners and researchers alike in the
increasingly important area of private graph data release and analysis
A Comprehensive Bibliometric Analysis on Social Network Anonymization: Current Approaches and Future Directions
In recent decades, social network anonymization has become a crucial research
field due to its pivotal role in preserving users' privacy. However, the high
diversity of approaches introduced in relevant studies poses a challenge to
gaining a profound understanding of the field. In response to this, the current
study presents an exhaustive and well-structured bibliometric analysis of the
social network anonymization field. To begin our research, related studies from
the period of 2007-2022 were collected from the Scopus Database then
pre-processed. Following this, the VOSviewer was used to visualize the network
of authors' keywords. Subsequently, extensive statistical and network analyses
were performed to identify the most prominent keywords and trending topics.
Additionally, the application of co-word analysis through SciMAT and the
Alluvial diagram allowed us to explore the themes of social network
anonymization and scrutinize their evolution over time. These analyses
culminated in an innovative taxonomy of the existing approaches and
anticipation of potential trends in this domain. To the best of our knowledge,
this is the first bibliometric analysis in the social network anonymization
field, which offers a deeper understanding of the current state and an
insightful roadmap for future research in this domain.Comment: 73 pages, 28 figure
A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability
Graph Neural Networks (GNNs) have made rapid developments in the recent
years. Due to their great ability in modeling graph-structured data, GNNs are
vastly used in various applications, including high-stakes scenarios such as
financial analysis, traffic predictions, and drug discovery. Despite their
great potential in benefiting humans in the real world, recent study shows that
GNNs can leak private information, are vulnerable to adversarial attacks, can
inherit and magnify societal bias from training data and lack interpretability,
which have risk of causing unintentional harm to the users and society. For
example, existing works demonstrate that attackers can fool the GNNs to give
the outcome they desire with unnoticeable perturbation on training graph. GNNs
trained on social networks may embed the discrimination in their decision
process, strengthening the undesirable societal bias. Consequently, trustworthy
GNNs in various aspects are emerging to prevent the harm from GNN models and
increase the users' trust in GNNs. In this paper, we give a comprehensive
survey of GNNs in the computational aspects of privacy, robustness, fairness,
and explainability. For each aspect, we give the taxonomy of the related
methods and formulate the general frameworks for the multiple categories of
trustworthy GNNs. We also discuss the future research directions of each aspect
and connections between these aspects to help achieve trustworthiness
PrivGraph: Differentially Private Graph Data Publication by Exploiting Community Information
Graph data is used in a wide range of applications, while analyzing graph
data without protection is prone to privacy breach risks. To mitigate the
privacy risks, we resort to the standard technique of differential privacy to
publish a synthetic graph. However, existing differentially private graph
synthesis approaches either introduce excessive noise by directly perturbing
the adjacency matrix, or suffer significant information loss during the graph
encoding process. In this paper, we propose an effective graph synthesis
algorithm PrivGraph by exploiting the community information. Concretely,
PrivGraph differentially privately partitions the private graph into
communities, extracts intra-community and inter-community information, and
reconstructs the graph from the extracted graph information. We validate the
effectiveness of PrivGraph on six real-world graph datasets and seven commonly
used graph metrics.Comment: To Appear in the 32nd USENIX Security Symposiu
Adversarial Robustness in Unsupervised Machine Learning: A Systematic Review
As the adoption of machine learning models increases, ensuring robust models
against adversarial attacks is increasingly important. With unsupervised
machine learning gaining more attention, ensuring it is robust against attacks
is vital. This paper conducts a systematic literature review on the robustness
of unsupervised learning, collecting 86 papers. Our results show that most
research focuses on privacy attacks, which have effective defenses; however,
many attacks lack effective and general defensive measures. Based on the
results, we formulate a model on the properties of an attack on unsupervised
learning, contributing to future research by providing a model to use.Comment: 38 pages, 11 figure
Privacy Protection and Utility Trade-Off for Social Graph Embedding
In graph embedding protection, deleting the embedding vector of a node does not completelydisrupt its structural relationships. The embedding model must be retrained over the networkwithout sensitive nodes, which incurs a waste of computation and offers no protection forordinary users. Meanwhile, the edge perturbations do not guarantee good utility. This workproposed a new privacy protection and utility trade-off method without retraining. Firstly, sinceembedding distance reflects the closeness of nodes, we label and group user nodes into sensitive,near-sensitive, and ordinary regions to perform different strengths of privacy protection. Thenear-sensitive region can reduce the leaking risk of neighboring nodes connecting to sensitivenodes without sacrificing all of their utility. Secondly, we use mutual information to measureprivacy and utility while adapting a single model-based mutual information neural estimatorto vector pairs to reduce modeling and computational complexity. Thirdly, by keeping addingdifferent noise to the divided regions and reestimating the mutual information between theoriginal and noise-perturbed embeddings, our framework achieves a good trade-off betweenprivacy and utility. Simulation results show that the proposed framework is superior to state-of-the-art baselines like LPPGE and DPNE
Towards Mobility Data Science (Vision Paper)
Mobility data captures the locations of moving objects such as humans,
animals, and cars. With the availability of GPS-equipped mobile devices and
other inexpensive location-tracking technologies, mobility data is collected
ubiquitously. In recent years, the use of mobility data has demonstrated
significant impact in various domains including traffic management, urban
planning, and health sciences. In this paper, we present the emerging domain of
mobility data science. Towards a unified approach to mobility data science, we
envision a pipeline having the following components: mobility data collection,
cleaning, analysis, management, and privacy. For each of these components, we
explain how mobility data science differs from general data science, we survey
the current state of the art and describe open challenges for the research
community in the coming years.Comment: Updated arXiv metadata to include two authors that were missing from
the metadata. PDF has not been change
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