27,975 research outputs found
Balancing Privacy and Accuracy in IoT using Domain-Specific Features for Time Series Classification
ε-Differential Privacy (DP) has been popularly used for anonymizing data to protect sensitive information and for machine learning (ML) tasks. However, there is a trade-off in balancing privacy and achieving ML accuracy since ε-DP reduces the model’s accuracy for classification tasks. Moreover, not many studies have applied DP to time series from sensors and Internet-of-Things (IoT) devices. In this work, we try to achieve the accuracy of ML models trained with ε-DP data to be as close to the ML models trained with non-anonymized data for two different physiological time series. We propose to transform time series into domain-specific 2D (image) representations such as scalograms, recurrence plots (RP), and their joint representation as inputs for training classifiers. The advantages of using these image representations render our proposed approach secure by preventing data leaks since these image transformations are irreversible. These images allow us to apply state-of-the-art image classifiers to obtain accuracy comparable to classifiers trained on non-anonymized data by ex- ploiting the additional information such as textured patterns from these images. In order to achieve classifier performance with anonymized data close to non-anonymized data, it is important to identify the value of ε and the input feature. Experimental results demonstrate that the performance of the ML models with scalograms and RP was comparable to ML models trained on their non-anonymized versions. Motivated by the promising results, an end-to-end IoT ML edge-cloud architecture capable of detecting input drifts is designed that employs our technique to train ML models on ε-DP physiological data. Our classification approach ensures the privacy of individuals while processing and analyzing the data at the edge securely and efficiently
Lacunar infarcts, depression and anxiety symptoms one year after stroke
Background:
Mood disorders are frequent after stroke and are associated with poorer quality of life. Previous studies have reported conflicting results as to stroke subtype in the incidence of poststroke mood disorders. We explored the relationship between subcortical ischemic stroke subtype (lacunar) and presence of such symptoms at 1 year after stroke.
Methods:
Anonymized data were accessed from the Virtual International Stroke Trials Archive. Stroke subtypes were classified according to the Trial of Org 10172 in Acute Stroke Treatment classification. Depression and anxiety symptoms were assessed using Hospital Anxiety and Depression Scale. We investigated independent predictors of depression and anxiety symptoms using a logistic regression model.
Results:
Data were available for 2160 patients. Almost one fifth of the patients developed both anxiety and depression at 1-year follow-up. After adjusting for confounders, the lacunar subtype was least associated with both anxiety (odds ratio [OR] = .61; 95% confidence interval [CI] = .46-.80) and depression symptoms (OR = .71; CI = .55-.93) versus other stroke subtypes.
Conclusions:
Lacunar strokes have a weaker association with presence of anxiety and depression symptoms compared with other subtypes
An Automated Social Graph De-anonymization Technique
We present a generic and automated approach to re-identifying nodes in
anonymized social networks which enables novel anonymization techniques to be
quickly evaluated. It uses machine learning (decision forests) to matching
pairs of nodes in disparate anonymized sub-graphs. The technique uncovers
artefacts and invariants of any black-box anonymization scheme from a small set
of examples. Despite a high degree of automation, classification succeeds with
significant true positive rates even when small false positive rates are
sought. Our evaluation uses publicly available real world datasets to study the
performance of our approach against real-world anonymization strategies, namely
the schemes used to protect datasets of The Data for Development (D4D)
Challenge. We show that the technique is effective even when only small numbers
of samples are used for training. Further, since it detects weaknesses in the
black-box anonymization scheme it can re-identify nodes in one social network
when trained on another.Comment: 12 page
Name Disambiguation from link data in a collaboration graph using temporal and topological features
In a social community, multiple persons may share the same name, phone number
or some other identifying attributes. This, along with other phenomena, such as
name abbreviation, name misspelling, and human error leads to erroneous
aggregation of records of multiple persons under a single reference. Such
mistakes affect the performance of document retrieval, web search, database
integration, and more importantly, improper attribution of credit (or blame).
The task of entity disambiguation partitions the records belonging to multiple
persons with the objective that each decomposed partition is composed of
records of a unique person. Existing solutions to this task use either
biographical attributes, or auxiliary features that are collected from external
sources, such as Wikipedia. However, for many scenarios, such auxiliary
features are not available, or they are costly to obtain. Besides, the attempt
of collecting biographical or external data sustains the risk of privacy
violation. In this work, we propose a method for solving entity disambiguation
task from link information obtained from a collaboration network. Our method is
non-intrusive of privacy as it uses only the time-stamped graph topology of an
anonymized network. Experimental results on two real-life academic
collaboration networks show that the proposed method has satisfactory
performance.Comment: The short version of this paper has been accepted to ASONAM 201
Privacy- and Utility-Preserving NLP with Anonymized Data: A case study of Pseudonymization
This work investigates the effectiveness of different pseudonymization
techniques, ranging from rule-based substitutions to using pre-trained Large
Language Models (LLMs), on a variety of datasets and models used for two widely
used NLP tasks: text classification and summarization. Our work provides
crucial insights into the gaps between original and anonymized data (focusing
on the pseudonymization technique) and model quality and fosters future
research into higher-quality anonymization techniques to better balance the
trade-offs between data protection and utility preservation. We make our code,
pseudonymized datasets, and downstream models publicly availableComment: 10 pages. Accepted for TrustNLP workshop at ACL202
CIAGAN: Conditional Identity Anonymization Generative Adversarial Networks
The unprecedented increase in the usage of computer vision technology in
society goes hand in hand with an increased concern in data privacy. In many
real-world scenarios like people tracking or action recognition, it is
important to be able to process the data while taking careful consideration in
protecting people's identity. We propose and develop CIAGAN, a model for image
and video anonymization based on conditional generative adversarial networks.
Our model is able to remove the identifying characteristics of faces and bodies
while producing high-quality images and videos that can be used for any
computer vision task, such as detection or tracking. Unlike previous methods,
we have full control over the de-identification (anonymization) procedure,
ensuring both anonymization as well as diversity. We compare our method to
several baselines and achieve state-of-the-art results.Comment: CVPR 202
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