2,271 research outputs found
Link Prediction by De-anonymization: How We Won the Kaggle Social Network Challenge
This paper describes the winning entry to the IJCNN 2011 Social Network
Challenge run by Kaggle.com. The goal of the contest was to promote research on
real-world link prediction, and the dataset was a graph obtained by crawling
the popular Flickr social photo sharing website, with user identities scrubbed.
By de-anonymizing much of the competition test set using our own Flickr crawl,
we were able to effectively game the competition. Our attack represents a new
application of de-anonymization to gaming machine learning contests, suggesting
changes in how future competitions should be run.
We introduce a new simulated annealing-based weighted graph matching
algorithm for the seeding step of de-anonymization. We also show how to combine
de-anonymization with link prediction---the latter is required to achieve good
performance on the portion of the test set not de-anonymized---for example by
training the predictor on the de-anonymized portion of the test set, and
combining probabilistic predictions from de-anonymization and link prediction.Comment: 11 pages, 13 figures; submitted to IJCNN'201
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
Traveling Salesman Problem
This book is a collection of current research in the application of evolutionary algorithms and other optimal algorithms to solving the TSP problem. It brings together researchers with applications in Artificial Immune Systems, Genetic Algorithms, Neural Networks and Differential Evolution Algorithm. Hybrid systems, like Fuzzy Maps, Chaotic Maps and Parallelized TSP are also presented. Most importantly, this book presents both theoretical as well as practical applications of TSP, which will be a vital tool for researchers and graduate entry students in the field of applied Mathematics, Computing Science and Engineering
Differentially Private Empirical Risk Minimization
Privacy-preserving machine learning algorithms are crucial for the
increasingly common setting in which personal data, such as medical or
financial records, are analyzed. We provide general techniques to produce
privacy-preserving approximations of classifiers learned via (regularized)
empirical risk minimization (ERM). These algorithms are private under the
-differential privacy definition due to Dwork et al. (2006). First we
apply the output perturbation ideas of Dwork et al. (2006), to ERM
classification. Then we propose a new method, objective perturbation, for
privacy-preserving machine learning algorithm design. This method entails
perturbing the objective function before optimizing over classifiers. If the
loss and regularizer satisfy certain convexity and differentiability criteria,
we prove theoretical results showing that our algorithms preserve privacy, and
provide generalization bounds for linear and nonlinear kernels. We further
present a privacy-preserving technique for tuning the parameters in general
machine learning algorithms, thereby providing end-to-end privacy guarantees
for the training process. We apply these results to produce privacy-preserving
analogues of regularized logistic regression and support vector machines. We
obtain encouraging results from evaluating their performance on real
demographic and benchmark data sets. Our results show that both theoretically
and empirically, objective perturbation is superior to the previous
state-of-the-art, output perturbation, in managing the inherent tradeoff
between privacy and learning performance.Comment: 40 pages, 7 figures, accepted to the Journal of Machine Learning
Researc
Disease diagnosis in smart healthcare: Innovation, technologies and applications
To promote sustainable development, the smart city implies a global vision that merges artificial intelligence, big data, decision making, information and communication technology (ICT), and the internet-of-things (IoT). The ageing issue is an aspect that researchers, companies and government should devote efforts in developing smart healthcare innovative technology and applications. In this paper, the topic of disease diagnosis in smart healthcare is reviewed. Typical emerging optimization algorithms and machine learning algorithms are summarized. Evolutionary optimization, stochastic optimization and combinatorial optimization are covered. Owning to the fact that there are plenty of applications in healthcare, four applications in the field of diseases diagnosis (which also list in the top 10 causes of global death in 2015), namely cardiovascular diseases, diabetes mellitus, Alzheimer’s disease and other forms of dementia, and tuberculosis, are considered. In addition, challenges in the deployment of disease diagnosis in healthcare have been discussed
Tight Lower Bounds for Differentially Private Selection
A pervasive task in the differential privacy literature is to select the
items of "highest quality" out of a set of items, where the quality of each
item depends on a sensitive dataset that must be protected. Variants of this
task arise naturally in fundamental problems like feature selection and
hypothesis testing, and also as subroutines for many sophisticated
differentially private algorithms.
The standard approaches to these tasks---repeated use of the exponential
mechanism or the sparse vector technique---approximately solve this problem
given a dataset of samples. We provide a tight lower
bound for some very simple variants of the private selection problem. Our lower
bound shows that a sample of size is required
even to achieve a very minimal accuracy guarantee.
Our results are based on an extension of the fingerprinting method to sparse
selection problems. Previously, the fingerprinting method has been used to
provide tight lower bounds for answering an entire set of queries, but
often only some much smaller set of queries are relevant. Our extension
allows us to prove lower bounds that depend on both the number of relevant
queries and the total number of queries
- …