434 research outputs found
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Information reliability in complex multitask networks
The emergence of distributed and complex networks has altered the field of information and data processing in the past few years. In distributed networks, the connected neighboring nodes can cooperate and share information with each other in order to solve particular tasks. However, in many applications the agents might be reluctant to share their true data with all their neighbors due to privacy and security constraints. In this paper, we study the performance of multitask distributed networks where sharing genuine information is subject to a cost. We formulate an information credibility model which results in the probability of sharing genuine information at each time instant according to the cost. Each agent then shares its true information with only a subset of its neighbors while sending fabricated data to the rest according to this probability. This behavior can affect the performance of the whole network in an adverse manner especially in cases where the cost is high. To overcome this problem, we propose an adaptive reputation protocol which enables the agents to evaluate the behavior of their neighbors over time and select the most reputable subset of neighbors to share genuine information with. We provide an extensive simulation-based analysis to compare the performance of the proposed method with several other distributed learning strategies. The results show that the proposed method outperforms the other learning strategies and enables the network to have a superior performance especially when the cost of sharing genuine information is high
Stochastic Subgradient Algorithms for Strongly Convex Optimization over Distributed Networks
We study diffusion and consensus based optimization of a sum of unknown
convex objective functions over distributed networks. The only access to these
functions is through stochastic gradient oracles, each of which is only
available at a different node, and a limited number of gradient oracle calls is
allowed at each node. In this framework, we introduce a convex optimization
algorithm based on the stochastic gradient descent (SGD) updates. Particularly,
we use a carefully designed time-dependent weighted averaging of the SGD
iterates, which yields a convergence rate of
after gradient updates for each node on
a network of nodes. We then show that after gradient oracle calls, the
average SGD iterate achieves a mean square deviation (MSD) of
. This rate of convergence is optimal as it
matches the performance lower bound up to constant terms. Similar to the SGD
algorithm, the computational complexity of the proposed algorithm also scales
linearly with the dimensionality of the data. Furthermore, the communication
load of the proposed method is the same as the communication load of the SGD
algorithm. Thus, the proposed algorithm is highly efficient in terms of
complexity and communication load. We illustrate the merits of the algorithm
with respect to the state-of-art methods over benchmark real life data sets and
widely studied network topologies
Facial Data Minimization: Shallow Model as Your Privacy Filter
Face recognition service has been used in many fields and brings much
convenience to people. However, once the user's facial data is transmitted to a
service provider, the user will lose control of his/her private data. In recent
years, there exist various security and privacy issues due to the leakage of
facial data. Although many privacy-preserving methods have been proposed, they
usually fail when they are not accessible to adversaries' strategies or
auxiliary data. Hence, in this paper, by fully considering two cases of
uploading facial images and facial features, which are very typical in face
recognition service systems, we proposed a data privacy minimization
transformation (PMT) method. This method can process the original facial data
based on the shallow model of authorized services to obtain the obfuscated
data. The obfuscated data can not only maintain satisfactory performance on
authorized models and restrict the performance on other unauthorized models but
also prevent original privacy data from leaking by AI methods and human visual
theft. Additionally, since a service provider may execute preprocessing
operations on the received data, we also propose an enhanced perturbation
method to improve the robustness of PMT. Besides, to authorize one facial image
to multiple service models simultaneously, a multiple restriction mechanism is
proposed to improve the scalability of PMT. Finally, we conduct extensive
experiments and evaluate the effectiveness of the proposed PMT in defending
against face reconstruction, data abuse, and face attribute estimation attacks.
These experimental results demonstrate that PMT performs well in preventing
facial data abuse and privacy leakage while maintaining face recognition
accuracy.Comment: 14 pages, 11 figure
Networked Signal and Information Processing
The article reviews significant advances in networked signal and information
processing, which have enabled in the last 25 years extending decision making
and inference, optimization, control, and learning to the increasingly
ubiquitous environments of distributed agents. As these interacting agents
cooperate, new collective behaviors emerge from local decisions and actions.
Moreover, and significantly, theory and applications show that networked
agents, through cooperation and sharing, are able to match the performance of
cloud or federated solutions, while offering the potential for improved
privacy, increasing resilience, and saving resources
Quality of Information in Mobile Crowdsensing: Survey and Research Challenges
Smartphones have become the most pervasive devices in people's lives, and are
clearly transforming the way we live and perceive technology. Today's
smartphones benefit from almost ubiquitous Internet connectivity and come
equipped with a plethora of inexpensive yet powerful embedded sensors, such as
accelerometer, gyroscope, microphone, and camera. This unique combination has
enabled revolutionary applications based on the mobile crowdsensing paradigm,
such as real-time road traffic monitoring, air and noise pollution, crime
control, and wildlife monitoring, just to name a few. Differently from prior
sensing paradigms, humans are now the primary actors of the sensing process,
since they become fundamental in retrieving reliable and up-to-date information
about the event being monitored. As humans may behave unreliably or
maliciously, assessing and guaranteeing Quality of Information (QoI) becomes
more important than ever. In this paper, we provide a new framework for
defining and enforcing the QoI in mobile crowdsensing, and analyze in depth the
current state-of-the-art on the topic. We also outline novel research
challenges, along with possible directions of future work.Comment: To appear in ACM Transactions on Sensor Networks (TOSN
HFORD: High-Fidelity and Occlusion-Robust De-identification for Face Privacy Protection
With the popularity of smart devices and the development of computer vision
technology, concerns about face privacy protection are growing. The face
de-identification technique is a practical way to solve the identity protection
problem. The existing facial de-identification methods have revealed several
problems, including the impact on the realism of anonymized results when faced
with occlusions and the inability to maintain identity-irrelevant details in
anonymized results. We present a High-Fidelity and Occlusion-Robust
De-identification (HFORD) method to deal with these issues. This approach can
disentangle identities and attributes while preserving image-specific details
such as background, facial features (e.g., wrinkles), and lighting, even in
occluded scenes. To disentangle the latent codes in the GAN inversion space, we
introduce an Identity Disentanglement Module (IDM). This module selects the
latent codes that are closely related to the identity. It further separates the
latent codes into identity-related codes and attribute-related codes, enabling
the network to preserve attributes while only modifying the identity. To ensure
the preservation of image details and enhance the network's robustness to
occlusions, we propose an Attribute Retention Module (ARM). This module
adaptively preserves identity-irrelevant details and facial occlusions and
blends them into the generated results in a modulated manner. Extensive
experiments show that our method has higher quality, better detail fidelity,
and stronger occlusion robustness than other face de-identification methods
Orthogonal Subspace Learning for Language Model Continual Learning
Benefiting from massive corpora and advanced hardware, large language models
(LLMs) exhibit remarkable capabilities in language understanding and
generation. However, their performance degrades in scenarios where multiple
tasks are encountered sequentially, also known as catastrophic forgetting. In
this paper, we propose orthogonal low-rank adaptation (O-LoRA), a simple and
efficient approach for continual learning in language models, effectively
mitigating catastrophic forgetting while learning new tasks. Specifically,
O-LoRA learns tasks in different (low-rank) vector subspaces that are kept
orthogonal to each other in order to minimize interference. Our method induces
only marginal additional parameter costs and requires no user data storage for
replay. Experimental results on continual learning benchmarks show that our
method outperforms state-of-the-art methods. Furthermore, compared to previous
approaches, our method excels in preserving the generalization ability of LLMs
on unseen tasks.Comment: EMNLP 2023 finding
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