284 research outputs found
Optimal subset-difference broadcast encryption with free riders
Cataloged from PDF version of article.Broadcast encryption (BE) deals with secure transmission of a message to a group of receivers such that only an authorized subset of receivers can decrypt the message. The transmission cost of a BE system can be reduced considerably if a limited number of free riders can be tolerated in the system. in this paper, we study the problem of how to optimally place a given number of free riders in a subset-difference (SD)-based BE system, which is currently the most efficient BE scheme in use and has also been incorporated in standards, and we propose a polynomial-time optimal placement algorithm and three more efficient heuristics for this problem. Simulation experiments show that SD-based BE schemes can benefit significantly from the proposed algorithms. (C) 2009 Elsevier Inc. All rights reserved
Optimization techniques and new methods for boradcast encryption and traitor tracing schemes
Ankara : The Department of Computer Engineering and the Graduate School of Engineering and Science of Bilkent University, 2012.Thesis (Ph. D.) -- Bilkent University, 2012.Includes bibliographical refences.In the last few decades, the use of digital content increased dramatically. Many
forms of digital products in the form of CDs, DVDs, TV broadcasts, data over
the Internet, entered our life. Classical cryptography, where encryption is
done for only one recipient, was not able to handle this change, since its direct
use leads to intolerably expensive transmissions. Moreover, new concerns
regarding the commercial aspect arised. Since digital commercial contents are
sold to various customers, unauthorized copying by malicious actors became
a major concern and it needed to be prevented carefully. Therefore, a new
research area called digital rights management (DRM) has emerged. Within
the scope of DRM, new cryptographic primitives are proposed. In this thesis,
we consider three of these: broadcast encryption (BE), traitor tracing (TT),
and trace and revoke (T&R) schemes and propose methods to improve the performances
and capabilities of these primitives. Particularly, we first consider
profiling the recipient set in order to improve transmission size in the most
popular BE schemes. We then investigate and solve the optimal free rider
assignment problem for one of the most efficient BE schemes so far. Next, we
attempt to close the non-trivial gap between BE and T&R schemes by proposing
a generic method for adding traitor tracing capability to BE schemes and
thus obtaining a T&R scheme. Finally, we investigate an overlooked problem:
privacy of the recipient set in T&R schemes. Right now, most schemes do not
keep the recipient set anonymous, and everybody can see who received a particular
content. As a generic solution to this problem, we propose a method
for obtaining anonymous T&R scheme by using anonymous BE schemes as a
primitive.Ak, MuratPh.D
Privacy and Robustness in Federated Learning: Attacks and Defenses
As data are increasingly being stored in different silos and societies
becoming more aware of data privacy issues, the traditional centralized
training of artificial intelligence (AI) models is facing efficiency and
privacy challenges. Recently, federated learning (FL) has emerged as an
alternative solution and continue to thrive in this new reality. Existing FL
protocol design has been shown to be vulnerable to adversaries within or
outside of the system, compromising data privacy and system robustness. Besides
training powerful global models, it is of paramount importance to design FL
systems that have privacy guarantees and are resistant to different types of
adversaries. In this paper, we conduct the first comprehensive survey on this
topic. Through a concise introduction to the concept of FL, and a unique
taxonomy covering: 1) threat models; 2) poisoning attacks and defenses against
robustness; 3) inference attacks and defenses against privacy, we provide an
accessible review of this important topic. We highlight the intuitions, key
techniques as well as fundamental assumptions adopted by various attacks and
defenses. Finally, we discuss promising future research directions towards
robust and privacy-preserving federated learning.Comment: arXiv admin note: text overlap with arXiv:2003.02133; text overlap
with arXiv:1911.11815 by other author
A key Management Scheme for Access Control to GNSS Services
Conditional access is a challenging problem in GNSS scenarios. Most key management schemes present in literature can not cope with all GNSS related issues, such as extremely low bandwidth, stateless receivers and the absence of an aiding channel. After assessing existing techniques, a novel key management scheme called RevHash has been devised with particular emphasis on guaranteeing revocation capabilities to the system, in order for it to be robust against anomalies and attacks
Harnessing the Power of Distributed Computing: Advancements in Scientific Applications, Homomorphic Encryption, and Federated Learning Security
Data explosion poses lot of challenges to the state-of-the art systems, applications, and methodologies. It has been reported that 181 zettabytes of data are expected to be generated in 2025 which is over 150\% increase compared to the data that is expected to be generated in 2023. However, while system manufacturers are consistently developing devices with larger storage spaces and providing alternative storage capacities in the cloud at affordable rates, another key challenge experienced is how to effectively process the fraction of large scale of stored data in time-critical conventional systems. One transformative paradigm revolutionizing the processing and management of these large data is distributed computing whose application requires deep understanding. This dissertation focuses on exploring the potential impact of applying efficient distributed computing concepts to long existing challenges or issues in (i) a widely data-intensive scientific application (ii) applying homomorphic encryption to data intensive workloads found in outsourced databases and (iii) security of tokenized incentive mechanism for Federated learning (FL) systems.The first part of the dissertation tackles the Microelectrode arrays (MEAs) parameterization problem from an orthogonal viewpoint enlightened by algebraic topology, which allows us to algebraically parametrize MEAs whose structure and intrinsic parallelism are hard to identify otherwise. We implement a new paradigm, namely Parma, to demonstrate the effectiveness of the proposed approach and report how it outperforms the state-of-the-practice in time, scalability, and memory usage.The second part discusses our work on introducing the concept of parallel caching of secure aggregation to mitigate the performance overhead incurred by the HE module in outsourced databases. The key idea of this optimization approach is caching selected radix-ciphertexts in parallel without violating existing security guarantees of the primitive/base HE scheme. A new radix HE algorithm was designed and applied to both batch and incremental HE schemes, and experiments carried out on six workloads show that the proposed caching boost state-of-the-art HE schemes by high orders of magnitudes.In the third part, I will discuss our work on leveraging the security benefit of blockchains to enhance or protect the fairness and reliability of tokenized incentive mechanism for FL systems. We designed a blockchain-based auditing protocol to mitigate Gaussian attacks and carried out experiments with multiple FL aggregation algorithms, popular data sets and a variety of scales to validate its effectiveness
Towards Cyber Security for Low-Carbon Transportation: Overview, Challenges and Future Directions
In recent years, low-carbon transportation has become an indispensable part
as sustainable development strategies of various countries, and plays a very
important responsibility in promoting low-carbon cities. However, the security
of low-carbon transportation has been threatened from various ways. For
example, denial of service attacks pose a great threat to the electric vehicles
and vehicle-to-grid networks. To minimize these threats, several methods have
been proposed to defense against them. Yet, these methods are only for certain
types of scenarios or attacks. Therefore, this review addresses security aspect
from holistic view, provides the overview, challenges and future directions of
cyber security technologies in low-carbon transportation. Firstly, based on the
concept and importance of low-carbon transportation, this review positions the
low-carbon transportation services. Then, with the perspective of network
architecture and communication mode, this review classifies its typical attack
risks. The corresponding defense technologies and relevant security suggestions
are further reviewed from perspective of data security, network management
security and network application security. Finally, in view of the long term
development of low-carbon transportation, future research directions have been
concerned.Comment: 34 pages, 6 figures, accepted by journal Renewable and Sustainable
Energy Review
Systems-compatible Incentives
Originally, the Internet was a technological playground, a collaborative endeavor among researchers who shared the common goal of achieving communication. Self-interest used not to be a concern, but the motivations of the Internet's participants have broadened. Today, the Internet consists of millions of commercial entities and nearly 2 billion users, who often have conflicting goals. For example, while Facebook gives users the illusion of access control, users do not have the ability to control how the personal data they upload is shared or sold by Facebook. Even in BitTorrent, where all users seemingly have the same motivation of downloading a file as quickly as possible, users can subvert the protocol to download more quickly without giving their fair share. These examples demonstrate that protocols that are merely technologically proficient are not enough. Successful networked systems must account for potentially competing interests.
In this dissertation, I demonstrate how to build systems that give users incentives to follow the systems' protocols. To achieve incentive-compatible systems, I apply mechanisms from game theory and auction theory to protocol design. This approach has been considered in prior literature, but unfortunately has resulted in few real, deployed systems with incentives to cooperate. I identify the primary challenge in applying mechanism design and game theory to large-scale systems: the goals and assumptions of economic mechanisms often do not match those of networked systems. For example, while auction theory may assume a centralized clearing house, there is no analog in a decentralized system seeking to avoid single points of failure or centralized policies. Similarly, game theory often assumes that each player is able to observe everyone else's actions, or at the very least know how many other players there are, but maintaining perfect system-wide information is impossible in most systems. In other words, not all incentive mechanisms are systems-compatible.
The main contribution of this dissertation is the design, implementation, and evaluation of various systems-compatible incentive mechanisms and their application to a wide range of deployable systems. These systems include BitTorrent, which is used to distribute a large file to a large number of downloaders, PeerWise, which leverages user cooperation to achieve lower latencies in Internet routing, and Hoodnets, a new system I present that allows users to share their cellular data access to obtain greater bandwidth on their mobile devices. Each of these systems represents a different point in the design space of systems-compatible incentives. Taken together, along with their implementations and evaluations, these systems demonstrate that systems-compatibility is crucial in achieving practical incentives in real systems. I present design principles outlining how to achieve systems-compatible incentives, which may serve an even broader range of systems than considered herein. I conclude this dissertation with what I consider to be the most important open problems in aligning the competing interests of the Internet's participants
RC-SSFL: Towards Robust and Communication-efficient Semi-supervised Federated Learning System
Federated Learning (FL) is an emerging decentralized artificial intelligence
paradigm, which promises to train a shared global model in high-quality while
protecting user data privacy. However, the current systems rely heavily on a
strong assumption: all clients have a wealth of ground truth labeled data,
which may not be always feasible in the real life. In this paper, we present a
practical Robust, and Communication-efficient Semi-supervised FL (RC-SSFL)
system design that can enable the clients to jointly learn a high-quality model
that is comparable to typical FL's performance. In this setting, we assume that
the client has only unlabeled data and the server has a limited amount of
labeled data. Besides, we consider malicious clients can launch poisoning
attacks to harm the performance of the global model. To solve this issue,
RC-SSFL employs a minimax optimization-based client selection strategy to
select the clients who hold high-quality updates and uses geometric median
aggregation to robustly aggregate model updates. Furthermore, RC-SSFL
implements a novel symmetric quantization method to greatly improve
communication efficiency. Extensive case studies on two real-world datasets
demonstrate that RC-SSFL can maintain the performance comparable to typical FL
in the presence of poisoning attacks and reduce communication overhead by
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