25,457 research outputs found
A Survey on the Security of Pervasive Online Social Networks (POSNs)
Pervasive Online Social Networks (POSNs) are the extensions of Online Social
Networks (OSNs) which facilitate connectivity irrespective of the domain and
properties of users. POSNs have been accumulated with the convergence of a
plethora of social networking platforms with a motivation of bridging their
gap. Over the last decade, OSNs have visually perceived an altogether
tremendous amount of advancement in terms of the number of users as well as
technology enablers. A single OSN is the property of an organization, which
ascertains smooth functioning of its accommodations for providing a quality
experience to their users. However, with POSNs, multiple OSNs have coalesced
through communities, circles, or only properties, which make
service-provisioning tedious and arduous to sustain. Especially, challenges
become rigorous when the focus is on the security perspective of cross-platform
OSNs, which are an integral part of POSNs. Thus, it is of utmost paramountcy to
highlight such a requirement and understand the current situation while
discussing the available state-of-the-art. With the modernization of OSNs and
convergence towards POSNs, it is compulsory to understand the impact and reach
of current solutions for enhancing the security of users as well as associated
services. This survey understands this requisite and fixates on different sets
of studies presented over the last few years and surveys them for their
applicability to POSNs...Comment: 39 Pages, 10 Figure
Emerging Privacy Issues and Solutions in Cyber-Enabled Sharing Services: From Multiple Perspectives
Fast development of sharing services has become a crucial part of the
cyber-enabled world construction process, as sharing services reinvent how
people exchange and obtain goods or services. However, privacy leakage or
disclosure remains a key concern during the sharing service development
process. While significant efforts have been undertaken to address various
privacy issues in recent years, there is a surprising lack of review for
privacy concerns in the cyber-enabled sharing world. To bridge the gap, in this
study, we survey and evaluate existing and emerging privacy issues relating to
sharing services from various perspectives. Differing from existing similar
works on surveying sharing practices in various fields, our work
comprehensively covers six branches of sharing services in the cyber-enabled
world and selects solutions mostly from the recent five to six years. We
conclude the issues and solutions from three perspectives, namely, from users',
platforms' and service providers' perspectives. Hot topics and less discussed
topics are identified, which provides hints to researchers for their future
studies.Comment: 28 pages, 13 figure
Privacy in Sensor-Driven Human Data Collection: A Guide for Practitioners
In recent years, the amount of information collected about human beings has
increased dramatically. This development has been partially driven by
individuals posting and storing data about themselves and friends using online
social networks or collecting their data for self-tracking purposes
(quantified-self movement). Across the sciences, researchers conduct studies
collecting data with an unprecedented resolution and scale. Using computational
power combined with mathematical models, such rich datasets can be mined to
infer underlying patterns, thereby providing insights into human nature. Much
of the data collected is sensitive. It is private in the sense that most
individuals would feel uncomfortable sharing their collected personal data
publicly. For this reason, the need for solutions to ensure the privacy of the
individuals generating data has grown alongside the data collection efforts.
Out of all the massive data collection efforts, this paper focuses on efforts
directly instrumenting human behavior, and notes that -- in many cases -- the
privacy of participants is not sufficiently addressed. For example, study
purposes are often not explicit, informed consent is ill-defined, and security
and sharing protocols are only partially disclosed. This paper provides a
survey of the work related to addressing privacy issues in research studies
that collect detailed sensor data on human behavior. Reflections on the key
problems and recommendations for future work are included. We hope the overview
of the privacy-related practices in massive data collection studies can be used
as a frame of reference for practitioners in the field. Although focused on
data collection in an academic context, we believe that many of the challenges
and solutions we identify are also relevant and useful for other domains where
massive data collection takes place, including businesses and governments
The Long Road to Computational Location Privacy: A Survey
The widespread adoption of continuously connected smartphones and tablets
developed the usage of mobile applications, among which many use location to
provide geolocated services. These services provide new prospects for users:
getting directions to work in the morning, leaving a check-in at a restaurant
at noon and checking next day's weather in the evening are possible right from
any mobile device embedding a GPS chip. In these location-based applications,
the user's location is sent to a server, which uses them to provide contextual
and personalised answers. However, nothing prevents the latter from gathering,
analysing and possibly sharing the collected information, which opens the door
to many privacy threats. Indeed, mobility data can reveal sensitive information
about users, among which one's home, work place or even religious and political
preferences. For this reason, many privacy-preserving mechanisms have been
proposed these last years to enhance location privacy while using geolocated
services. This article surveys and organises contributions in this area from
classical building blocks to the most recent developments of privacy threats
and location privacy-preserving mechanisms. We divide the protection mechanisms
between online and offline use cases, and organise them into six categories
depending on the nature of their algorithm. Moreover, this article surveys the
evaluation metrics used to assess protection mechanisms in terms of privacy,
utility and performance. Finally, open challenges and new directions to address
the problem of computational location privacy are pointed out and discussed.Comment: IEEE Communications Surveys & Tutorial
Image Privacy Prediction Using Deep Neural Networks
Images today are increasingly shared online on social networking sites such
as Facebook, Flickr, Foursquare, and Instagram. Despite that current social
networking sites allow users to change their privacy preferences, this is often
a cumbersome task for the vast majority of users on the Web, who face
difficulties in assigning and managing privacy settings. Thus, automatically
predicting images' privacy to warn users about private or sensitive content
before uploading these images on social networking sites has become a necessity
in our current interconnected world.
In this paper, we explore learning models to automatically predict
appropriate images' privacy as private or public using carefully identified
image-specific features. We study deep visual semantic features that are
derived from various layers of Convolutional Neural Networks (CNNs) as well as
textual features such as user tags and deep tags generated from deep CNNs.
Particularly, we extract deep (visual and tag) features from four pre-trained
CNN architectures for object recognition, i.e., AlexNet, GoogLeNet, VGG-16, and
ResNet, and compare their performance for image privacy prediction. Results of
our experiments on a Flickr dataset of over thirty thousand images show that
the learning models trained on features extracted from ResNet outperform the
state-of-the-art models for image privacy prediction. We further investigate
the combination of user tags and deep tags derived from CNN architectures using
two settings: (1) SVM on the bag-of-tags features; and (2) text-based CNN. Our
results show that even though the models trained on the visual features perform
better than those trained on the tag features, the combination of deep visual
features with image tags shows improvements in performance over the individual
feature sets
Security for 4G and 5G Cellular Networks: A Survey of Existing Authentication and Privacy-preserving Schemes
This paper presents a comprehensive survey of existing authentication and
privacy-preserving schemes for 4G and 5G cellular networks. We start by
providing an overview of existing surveys that deal with 4G and 5G
communications, applications, standardization, and security. Then, we give a
classification of threat models in 4G and 5G cellular networks in four
categories, including, attacks against privacy, attacks against integrity,
attacks against availability, and attacks against authentication. We also
provide a classification of countermeasures into three types of categories,
including, cryptography methods, humans factors, and intrusion detection
methods. The countermeasures and informal and formal security analysis
techniques used by the authentication and privacy preserving schemes are
summarized in form of tables. Based on the categorization of the authentication
and privacy models, we classify these schemes in seven types, including,
handover authentication with privacy, mutual authentication with privacy, RFID
authentication with privacy, deniable authentication with privacy,
authentication with mutual anonymity, authentication and key agreement with
privacy, and three-factor authentication with privacy. In addition, we provide
a taxonomy and comparison of authentication and privacy-preserving schemes for
4G and 5G cellular networks in form of tables. Based on the current survey,
several recommendations for further research are discussed at the end of this
paper.Comment: 24 pages, 14 figure
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
Albatross: a Privacy-Preserving Location Sharing System
Social networking services are increasingly accessed through mobile devices.
This trend has prompted services such as Facebook and Google+ to incorporate
location as a de facto feature of user interaction. At the same time, services
based on location such as Foursquare and Shopkick are also growing as
smartphone market penetration increases. In fact, this growth is happening
despite concerns (growing at a similar pace) about security and third-party use
of private location information (e.g., for advertising). Nevertheless, service
providers have been unwilling to build truly private systems in which they do
not have access to location information. In this paper, we describe an
architecture and a trial implementation of a privacy-preserving location
sharing system called Albatross. The system protects location information from
the service provider and yet enables fine-grained location-sharing. One main
feature of the system is to protect an individual's social network structure.
The pattern of location sharing preferences towards contacts can reveal this
structure without any knowledge of the locations themselves. Albatross protects
locations sharing preferences through protocol unification and masking.
Albatross has been implemented as a standalone solution, but the technology can
also be integrated into location-based services to enhance privacy.Comment: 12 Pages, Extended version of ASIACCS 2015 pape
Internet of Cloud: Security and Privacy issues
The synergy between the cloud and the IoT has emerged largely due to the
cloud having attributes which directly benefit the IoT and enable its continued
growth. IoT adopting Cloud services has brought new security challenges. In
this book chapter, we pursue two main goals: 1) to analyse the different
components of Cloud computing and the IoT and 2) to present security and
privacy problems that these systems face. We thoroughly investigate current
security and privacy preservation solutions that exist in this area, with an
eye on the Industrial Internet of Things, discuss open issues and propose
future directionsComment: 27 pages, 4 figure
Crowdsensing and privacy in smart city applications
Smartness in smart cities is achieved by sensing phenomena of interest and
using them to make smart decisions. Since the decision makers may not own all
the necessary sensing infrastructures, crowdsourced sensing, can help collect
important information of the city in near real-time. However, involving people
brings of the risk of exposing their private information.This chapter explores
crowdsensing in smart city applications and its privacy implications.Comment: Preprint submitted to Book: Smart Cities, Cybersecurity and Privacy,
Elsevie
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