7,682 research outputs found
Privacy-preserving human mobility and activity modelling
The exponential proliferation of digital trends and worldwide responses to the COVID-19 pandemic thrust the world into digitalization and interconnectedness, pushing increasingly new technologies/devices/applications into the market. More and more intimate data of users are collected for positive analysis purposes of improving living well-being but shared with/without the user's consent, emphasizing the importance of making human mobility and activity models inclusive, private, and fair. In this thesis, I develop and implement advanced methods/algorithms to model human mobility and activity in terms of temporal-context dynamics, multi-occupancy impacts, privacy protection, and fair analysis.
The following research questions have been thoroughly investigated: i) whether the temporal information integrated into the deep learning networks can improve the prediction accuracy in both predicting the next activity and its timing; ii) how is the trade-off between cost and performance when optimizing the sensor network for multiple-occupancy smart homes; iii) whether the malicious purposes such as user re-identification in human mobility modelling could be mitigated by adversarial learning; iv) whether the fairness implications of mobility models and whether privacy-preserving techniques perform equally for different groups of users.
To answer these research questions, I develop different architectures to model human activity and mobility. I first clarify the temporal-context dynamics in human activity modelling and achieve better prediction accuracy by appropriately using the temporal information. I then design a framework MoSen to simulate the interaction dynamics among residents and intelligent environments and generate an effective sensor network strategy. To relieve users' privacy concerns, I design Mo-PAE and show that the privacy of mobility traces attains decent protection at the marginal utility cost. Last but not least, I investigate the relations between fairness and privacy and conclude that while the privacy-aware model guarantees group fairness, it violates the individual fairness criteria.Open Acces
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
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
Directional Privacy for Deep Learning
Differentially Private Stochastic Gradient Descent (DP-SGD) is a key method
for applying privacy in the training of deep learning models. This applies
isotropic Gaussian noise to gradients during training, which can perturb these
gradients in any direction, damaging utility. Metric DP, however, can provide
alternative mechanisms based on arbitrary metrics that might be more suitable.
In this paper we apply \textit{directional privacy}, via a mechanism based on
the von Mises-Fisher (VMF) distribution, to perturb gradients in terms of
\textit{angular distance} so that gradient direction is broadly preserved. We
show that this provides -privacy for deep learning training, rather
than the -privacy of the Gaussian mechanism; and that
experimentally, on key datasets, the VMF mechanism can outperform the Gaussian
in the utility-privacy trade-off
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