12,359 research outputs found
Differential Privacy Techniques for Cyber Physical Systems: A Survey
Modern cyber physical systems (CPSs) has widely being used in our daily lives
because of development of information and communication technologies (ICT).With
the provision of CPSs, the security and privacy threats associated to these
systems are also increasing. Passive attacks are being used by intruders to get
access to private information of CPSs. In order to make CPSs data more secure,
certain privacy preservation strategies such as encryption, and k-anonymity
have been presented in the past. However, with the advances in CPSs
architecture, these techniques also needs certain modifications. Meanwhile,
differential privacy emerged as an efficient technique to protect CPSs data
privacy. In this paper, we present a comprehensive survey of differential
privacy techniques for CPSs. In particular, we survey the application and
implementation of differential privacy in four major applications of CPSs named
as energy systems, transportation systems, healthcare and medical systems, and
industrial Internet of things (IIoT). Furthermore, we present open issues,
challenges, and future research direction for differential privacy techniques
for CPSs. This survey can serve as basis for the development of modern
differential privacy techniques to address various problems and data privacy
scenarios of CPSs.Comment: 46 pages, 12 figure
A Random Matrix Approach to Differential Privacy and Structure Preserved Social Network Graph Publishing
Online social networks are being increasingly used for analyzing various
societal phenomena such as epidemiology, information dissemination, marketing
and sentiment flow. Popular analysis techniques such as clustering and
influential node analysis, require the computation of eigenvectors of the real
graph's adjacency matrix. Recent de-anonymization attacks on Netflix and AOL
datasets show that an open access to such graphs pose privacy threats. Among
the various privacy preserving models, Differential privacy provides the
strongest privacy guarantees.
In this paper we propose a privacy preserving mechanism for publishing social
network graph data, which satisfies differential privacy guarantees by
utilizing a combination of theory of random matrix and that of differential
privacy. The key idea is to project each row of an adjacency matrix to a low
dimensional space using the random projection approach and then perturb the
projected matrix with random noise. We show that as compared to existing
approaches for differential private approximation of eigenvectors, our approach
is computationally efficient, preserves the utility and satisfies differential
privacy. We evaluate our approach on social network graphs of Facebook, Live
Journal and Pokec. The results show that even for high values of noise variance
sigma=1 the clustering quality given by normalized mutual information gain is
as low as 0.74. For influential node discovery, the propose approach is able to
correctly recover 80 of the most influential nodes. We also compare our results
with an approach presented in [43], which directly perturbs the eigenvector of
the original data by a Laplacian noise. The results show that this approach
requires a large random perturbation in order to preserve the differential
privacy, which leads to a poor estimation of eigenvectors for large social
networks
Release Connection Fingerprints in Social Networks Using Personalized Differential Privacy
In social networks, different users may have different privacy preferences
and there are many users with public identities. Most work on differentially
private social network data publication neglects this fact. We aim to release
the number of public users that a private user connects to within n hops,
called n-range Connection fingerprints(CFPs), under user-level personalized
privacy preferences. We proposed two schemes, Distance-based exponential budget
absorption (DEBA) and Distance-based uniformly budget absorption using Ladder
function (DUBA-LF), for privacy-preserving publication of the CFPs based on
Personalized differential privacy(PDP), and we conducted a theoretical analysis
of the privacy guarantees provided within the proposed schemes. The
implementation showed that the proposed schemes are superior in publication
errors on real datasets.Comment: A short version of this paper is accepted for publication in Chinese
Journal of Electronic
Securing Social Media User Data - An Adversarial Approach
Social media users generate tremendous amounts of data. To better serve
users, it is required to share the user-related data among researchers,
advertisers and application developers. Publishing such data would raise more
concerns on user privacy. To encourage data sharing and mitigate user privacy
concerns, a number of anonymization and de-anonymization algorithms have been
developed to help protect privacy of social media users. In this work, we
propose a new adversarial attack specialized for social media data. We further
provide a principled way to assess effectiveness of anonymizing different
aspects of social media data. Our work sheds light on new privacy risks in
social media data due to innate heterogeneity of user-generated data which
require striking balance between sharing user data and protecting user privacy.Comment: Published in the 29th ACM Conference on Hypertext and Social Media,
Baltimore, MD, USA (HT-18
Differentially Private Continual Release of Graph Statistics
Motivated by understanding the dynamics of sensitive social networks over
time, we consider the problem of continual release of statistics in a network
that arrives online, while preserving privacy of its participants. For our
privacy notion, we use differential privacy -- the gold standard in privacy for
statistical data analysis. The main challenge in this problem is maintaining a
good privacy-utility tradeoff; naive solutions that compose across time, as
well as solutions suited to tabular data either lead to poor utility or do not
directly apply. In this work, we show that if there is a publicly known upper
bound on the maximum degree of any node in the entire network sequence, then we
can release many common graph statistics such as degree distributions and
subgraph counts continually with a better privacy-accuracy tradeoff.
Code available at https://bitbucket.org/shs037/graphprivacycod
Privacy-Preserving Collaborative Deep Learning with Unreliable Participants
With powerful parallel computing GPUs and massive user data,
neural-network-based deep learning can well exert its strong power in problem
modeling and solving, and has archived great success in many applications such
as image classification, speech recognition and machine translation etc. While
deep learning has been increasingly popular, the problem of privacy leakage
becomes more and more urgent. Given the fact that the training data may contain
highly sensitive information, e.g., personal medical records, directly sharing
them among the users (i.e., participants) or centrally storing them in one
single location may pose a considerable threat to user privacy.
In this paper, we present a practical privacy-preserving collaborative deep
learning system that allows users to cooperatively build a collective deep
learning model with data of all participants, without direct data sharing and
central data storage. In our system, each participant trains a local model with
their own data and only shares model parameters with the others. To further
avoid potential privacy leakage from sharing model parameters, we use
functional mechanism to perturb the objective function of the neural network in
the training process to achieve -differential privacy. In particular,
for the first time, we consider the existence of~\textit{unreliable
participants}, i.e., the participants with low-quality data, and propose a
solution to reduce the impact of these participants while protecting their
privacy. We evaluate the performance of our system on two well-known real-world
datasets for regression and classification tasks. The results demonstrate that
the proposed system is robust against unreliable participants, and achieves
high accuracy close to the model trained in a traditional centralized manner
while ensuring rigorous privacy protection
Social Networks Research Aspects: A Vast and Fast Survey Focused on the Issue of Privacy in Social Network Sites
The increasing participation of people in online activities in recent years
like content publishing, and having different kinds of relationships and
interactions, along with the emergence of online social networks and people's
extensive tendency toward them, have resulted in generation and availability of
a huge amount of valuable information that has never been available before, and
have introduced some new, attractive, varied, and useful research areas to
researchers. In this paper we try to review some of the accomplished research
on information of SNSs (Social Network Sites), and introduce some of the
attractive applications that analyzing this information has. This will lead to
the introduction of some new research areas to researchers. By reviewing the
research in this area we will present a categorization of research topics about
online social networks. This categorization includes seventeen research
subtopics or subareas that will be introduced along with some of the
accomplished research in these subareas. According to the consequences (slight,
significant, and sometimes catastrophic) that revelation of personal and
private information has, a research area that researchers have vastly
investigated is privacy in online social networks. After an overview on
different research subareas of SNSs, we will get more focused on the subarea of
privacy protection in social networks, and introduce different aspects of it
along with a categorization of these aspects
On the Computational Complexities of Three Privacy Measures for Large Networks Under Active Attack
With the arrival of modern internet era, large public networks of various
types have come to existence to benefit the society as a whole and several
research areas such as sociology, economics and geography in particular.
However, the societal and research benefits of these networks have also given
rise to potentially significant privacy issues in the sense that malicious
entities may violate the privacy of the users of such a network by analyzing
the network and deliberately using such privacy violations for deleterious
purposes. Such considerations have given rise to a new active research area
that deals with the quantification of privacy of users in large networks and
the corresponding investigation of computational complexity issues of computing
such quantified privacy measures. In this paper, we formalize three such
privacy measures for large networks and provide non-trivial theoretical
computational complexity results for computing these measures. Our results show
the first two measures can be computed efficiently, whereas the third measure
is provably hard to compute within a logarithmic approximation factor.
Furthermore, we also provide computational complexity results for the case when
the privacy requirement of the network is severely restricted, including an
efficient logarithmic approximation.Comment: 21 pages, 3 figure
Privacy in Social Media: Identification, Mitigation and Applications
The increasing popularity of social media has attracted a huge number of
people to participate in numerous activities on a daily basis. This results in
tremendous amounts of rich user-generated data. This data provides
opportunities for researchers and service providers to study and better
understand users' behaviors and further improve the quality of the personalized
services. Publishing user-generated data risks exposing individuals' privacy.
Users privacy in social media is an emerging task and has attracted increasing
attention in recent years. These works study privacy issues in social media
from the two different points of views: identification of vulnerabilities, and
mitigation of privacy risks. Recent research has shown the vulnerability of
user-generated data against the two general types of attacks, identity
disclosure and attribute disclosure. These privacy issues mandate social media
data publishers to protect users' privacy by sanitizing user-generated data
before publishing it. Consequently, various protection techniques have been
proposed to anonymize user-generated social media data. There is a vast
literature on privacy of users in social media from many perspectives. In this
survey, we review the key achievements of user privacy in social media. In
particular, we review and compare the state-of-the-art algorithms in terms of
the privacy leakage attacks and anonymization algorithms. We overview the
privacy risks from different aspects of social media and categorize the
relevant works into five groups 1) graph data anonymization and
de-anonymization, 2) author identification, 3) profile attribute disclosure, 4)
user location and privacy, and 5) recommender systems and privacy issues. We
also discuss open problems and future research directions for user privacy
issues in social media.Comment: This survey is currently under revie
Protecting User Privacy: An Approach for Untraceable Web Browsing History and Unambiguous User Profiles
The overturning of the Internet Privacy Rules by the Federal Communications
Commissions (FCC) in late March 2017 allows Internet Service Providers (ISPs)
to collect, share and sell their customers' Web browsing data without their
consent. With third-party trackers embedded on Web pages, this new rule has put
user privacy under more risk. The need arises for users on their own to protect
their Web browsing history from any potential adversaries. Although some
available solutions such as Tor, VPN, and HTTPS can help users conceal their
online activities, their use can also significantly hamper personalized online
services, i.e., degraded utility. In this paper, we design an effective Web
browsing history anonymization scheme, PBooster, aiming to protect users'
privacy while retaining the utility of their Web browsing history. The proposed
model pollutes users' Web browsing history by automatically inferring how many
and what links should be added to the history while addressing the
utility-privacy trade-off challenge. We conduct experiments to validate the
quality of the manipulated Web browsing history and examine the robustness of
the proposed approach for user privacy protection.Comment: This paper is accepted in the 12th ACM International Conference on
Web Search and Data Mining (WSDM-2019
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