190 research outputs found
CHORUS Deliverable 2.1: State of the Art on Multimedia Search Engines
Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socio-economic perspective.
The technical perspective includes an up to date view on content based indexing and retrieval technologies, multimedia search in the context of mobile devices and peer-to-peer networks, and an overview of current evaluation and benchmark inititiatives to measure the performance of multimedia search engines.
From a socio-economic perspective we inventorize the impact and legal consequences of these technical advances and point out future directions of research
Blockchain-assisted Undisclosed IIoT Vulnerabilities Trusted Sharing Protection with Dynamic Token
With the large-scale deployment of industrial internet of things (IIoT)
devices, the number of vulnerabilities that threaten IIoT security is also
growing dramatically, including a mass of undisclosed IIoT vulnerabilities that
lack mitigation measures. Coordination Vulnerabilities Disclosure (CVD) is one
of the most popular vulnerabilities sharing solutions, in which some security
workers (SWs) can develop undisclosed vulnerabilities patches together.
However, CVD assumes that sharing participants (SWs) are all honest, and thus
offering chances for dishonest SWs to leak undisclosed IIoT vulnerabilities. To
combat such threats, we propose an Undisclosed IIoT Vulnerabilities Trusted
Sharing Protection (UIV-TSP) scheme with dynamic token. In this article, a
dynamic token is an implicit access credential for an SW to acquire an
undisclosed vulnerability information, which is only held by the system and
constantly updated as the SW access. Meanwhile, the latest updated token can be
stealthily sneaked into the acquired information as the traceability token.
Once the undisclosed vulnerability information leaves the SW host, the embedded
self-destruct program will be automatically triggered to prevent leaks since
the destination MAC address in the traceability token has changed. To quickly
distinguish dishonest SWs, trust mechanism is adopted to evaluate the trust
value of SWs. Moreover, we design a blockchain-assisted continuous logs storage
method to achieve the tamper-proofing of dynamic token and the transparency of
undisclosed IIoT vulnerabilities sharing. The simulation results indicate that
our proposed scheme is resilient to suppress dishonest SWs and protect the IoT
undisclosed vulnerabilities effectively.Comment: 10 pages,12 figure
Model-based Approaches to Privacy Compliance
In the last decade, information technologies have been developing dramatically, and therefore data harvested via the Internet is growing rapidly. This technological change has a negative impact on privacy due to the sensitivity of the data collected and shared without convenient control or monitoring.\ua0The General Data Protection Regulation (GDPR) of the European Union has been in effect for more than three years, limiting how organizations collect, manage, and handle personal data. The GDPR poses both new challenges and opportunities for technological institutions. In this work, we address various aspects of privacy and propose approaches that can overcome some challenges of the GDPR.\ua0We focus on improving two currently adopted approaches to leverage them to enforce some of the GDPR\u27s requirements by design.\ua0The first part of this work is devoted to developing an access control model to effectively capture the nature of information accessed and shared in online social networks (OSNs).\ua0They might raise serious problems in what concerns users\u27 privacy. One privacy risk is caused by accessing and sharing co-owned data items, i.e., when a user posts a data item that involves other users, some users\u27 privacy might be disclosed. Another risk is caused by the privacy settings offered by OSNs that do not, in general, allow fine-grained enforcement.\ua0We propose a collaborative access control framework to deal with such privacy issues. We also present a proof-of-concept implementation of our approach.In the second part of the thesis, we adopt Data Flow Diagrams (DFDs) as a convenient representation to integrate privacy engineering activities into software design. DFDs are inadequate as a modeling tool for privacy, and there is a need to evolve them to be a privacy-aware approach.\ua0The first privacy-related lack that we solve is automatically inserting privacy requirements during design. Secondly, since DFDs have a hierarchical structure, we propose a refinement framework for DFDs that preserves structural and functional properties and the underlying privacy concepts. Finally, we take a step towards modeling privacy properties, and in particular purpose limitation, in DFDs, by defining a mathematical framework that elaborates how the purpose of a DFD should be specified, verified, or inferred. We provide proof-of-concept tools for all the proposed frameworks and evaluate them through case studies
Challenges and Remedies to Privacy and Security in AIGC: Exploring the Potential of Privacy Computing, Blockchain, and Beyond
Artificial Intelligence Generated Content (AIGC) is one of the latest
achievements in AI development. The content generated by related applications,
such as text, images and audio, has sparked a heated discussion. Various
derived AIGC applications are also gradually entering all walks of life,
bringing unimaginable impact to people's daily lives. However, the rapid
development of such generative tools has also raised concerns about privacy and
security issues, and even copyright issues in AIGC. We note that advanced
technologies such as blockchain and privacy computing can be combined with AIGC
tools, but no work has yet been done to investigate their relevance and
prospect in a systematic and detailed way. Therefore it is necessary to
investigate how they can be used to protect the privacy and security of data in
AIGC by fully exploring the aforementioned technologies. In this paper, we
first systematically review the concept, classification and underlying
technologies of AIGC. Then, we discuss the privacy and security challenges
faced by AIGC from multiple perspectives and purposefully list the
countermeasures that currently exist. We hope our survey will help researchers
and industry to build a more secure and robust AIGC system.Comment: 43 pages, 10 figure
Advances in Information Security and Privacy
With the recent pandemic emergency, many people are spending their days in smart working and have increased their use of digital resources for both work and entertainment. The result is that the amount of digital information handled online is dramatically increased, and we can observe a significant increase in the number of attacks, breaches, and hacks. This Special Issue aims to establish the state of the art in protecting information by mitigating information risks. This objective is reached by presenting both surveys on specific topics and original approaches and solutions to specific problems. In total, 16 papers have been published in this Special Issue
Data Hiding and Its Applications
Data hiding techniques have been widely used to provide copyright protection, data integrity, covert communication, non-repudiation, and authentication, among other applications. In the context of the increased dissemination and distribution of multimedia content over the internet, data hiding methods, such as digital watermarking and steganography, are becoming increasingly relevant in providing multimedia security. The goal of this book is to focus on the improvement of data hiding algorithms and their different applications (both traditional and emerging), bringing together researchers and practitioners from different research fields, including data hiding, signal processing, cryptography, and information theory, among others
Deep Learning for Free-Hand Sketch: A Survey
Free-hand sketches are highly illustrative, and have been widely used by
humans to depict objects or stories from ancient times to the present. The
recent prevalence of touchscreen devices has made sketch creation a much easier
task than ever and consequently made sketch-oriented applications increasingly
popular. The progress of deep learning has immensely benefited free-hand sketch
research and applications. This paper presents a comprehensive survey of the
deep learning techniques oriented at free-hand sketch data, and the
applications that they enable. The main contents of this survey include: (i) A
discussion of the intrinsic traits and unique challenges of free-hand sketch,
to highlight the essential differences between sketch data and other data
modalities, e.g., natural photos. (ii) A review of the developments of
free-hand sketch research in the deep learning era, by surveying existing
datasets, research topics, and the state-of-the-art methods through a detailed
taxonomy and experimental evaluation. (iii) Promotion of future work via a
discussion of bottlenecks, open problems, and potential research directions for
the community.Comment: This paper is accepted by IEEE TPAM
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