2,823 research outputs found
Source-independent quantum random number generation
Quantum random number generators can provide genuine randomness by appealing
to the fundamental principles of quantum mechanics. In general, a physical
generator contains two parts---a randomness source and its readout. The source
is essential to the quality of the resulting random numbers; hence, it needs to
be carefully calibrated and modeled to achieve information-theoretical provable
randomness. However, in practice, the source is a complicated physical system,
such as a light source or an atomic ensemble, and any deviations in the
real-life implementation from the theoretical model may affect the randomness
of the output. To close this gap, we propose a source-independent scheme for
quantum random number generation in which output randomness can be certified,
even when the source is uncharacterized and untrusted. In our randomness
analysis, we make no assumptions about the dimension of the source. For
instance, multiphoton emissions are allowed in optical implementations. Our
analysis takes into account the finite-key effect with the composable security
definition. In the limit of large data size, the length of the input random
seed is exponentially small compared to that of the output random bit. In
addition, by modifying a quantum key distribution system, we experimentally
demonstrate our scheme and achieve a randomness generation rate of over
bit/s.Comment: 11 pages, 7 figure
ANCHOR: logically-centralized security for Software-Defined Networks
While the centralization of SDN brought advantages such as a faster pace of
innovation, it also disrupted some of the natural defenses of traditional
architectures against different threats. The literature on SDN has mostly been
concerned with the functional side, despite some specific works concerning
non-functional properties like 'security' or 'dependability'. Though addressing
the latter in an ad-hoc, piecemeal way, may work, it will most likely lead to
efficiency and effectiveness problems. We claim that the enforcement of
non-functional properties as a pillar of SDN robustness calls for a systemic
approach. As a general concept, we propose ANCHOR, a subsystem architecture
that promotes the logical centralization of non-functional properties. To show
the effectiveness of the concept, we focus on 'security' in this paper: we
identify the current security gaps in SDNs and we populate the architecture
middleware with the appropriate security mechanisms, in a global and consistent
manner. Essential security mechanisms provided by anchor include reliable
entropy and resilient pseudo-random generators, and protocols for secure
registration and association of SDN devices. We claim and justify in the paper
that centralizing such mechanisms is key for their effectiveness, by allowing
us to: define and enforce global policies for those properties; reduce the
complexity of controllers and forwarding devices; ensure higher levels of
robustness for critical services; foster interoperability of the non-functional
property enforcement mechanisms; and promote the security and resilience of the
architecture itself. We discuss design and implementation aspects, and we prove
and evaluate our algorithms and mechanisms, including the formalisation of the
main protocols and the verification of their core security properties using the
Tamarin prover.Comment: 42 pages, 4 figures, 3 tables, 5 algorithms, 139 reference
Exploring Privacy Preservation in Outsourced K-Nearest Neighbors with Multiple Data Owners
The k-nearest neighbors (k-NN) algorithm is a popular and effective
classification algorithm. Due to its large storage and computational
requirements, it is suitable for cloud outsourcing. However, k-NN is often run
on sensitive data such as medical records, user images, or personal
information. It is important to protect the privacy of data in an outsourced
k-NN system.
Prior works have all assumed the data owners (who submit data to the
outsourced k-NN system) are a single trusted party. However, we observe that in
many practical scenarios, there may be multiple mutually distrusting data
owners. In this work, we present the first framing and exploration of privacy
preservation in an outsourced k-NN system with multiple data owners. We
consider the various threat models introduced by this modification. We discover
that under a particularly practical threat model that covers numerous
scenarios, there exists a set of adaptive attacks that breach the data privacy
of any exact k-NN system. The vulnerability is a result of the mathematical
properties of k-NN and its output. Thus, we propose a privacy-preserving
alternative system supporting kernel density estimation using a Gaussian
kernel, a classification algorithm from the same family as k-NN. In many
applications, this similar algorithm serves as a good substitute for k-NN. We
additionally investigate solutions for other threat models, often through
extensions on prior single data owner systems
Visual Model-Driven Design, Verification and Implementation of Security Protocols
A novel visual model-driven approach to security protocol design, verification, and implementation is presented in this paper. User-friendly graphical models are combined with rigorous formal methods to enable protocol verification and sound automatic code generation. Domain-specific abstractions keep the graphical models simple, yet powerful enough to represent complex, realistic protocols such as SSH. The main contribution is to bring together aspects that were only partially available or not available at all in previous proposal
Testing of PLL-based True Random Number Generator in Changing Working Conditions
Security of cryptographic systems depends significantly on security of secret keys. Unpredictability of the keys is achieved by their generation by True Random Number Generators (TRNGs). In the paper we analyze behavior of the Phase-Locked Loop (PLL) based TRNG in changing working environment. The frequency of signals synthesized by PLL may be naturally influenced by chip temperature. We show what impact the temperature has on the quality of generated random sequence of the PLL-based TRNG. Thank to analysis of internal signals of the generator we are able to prove dependencies between the PLL parameters, statistical parameters of the generated sequence and temperature. Considering the measured results of experiments we form a new requirement in order to improve the robustness of the designed TRNG
Group theory in cryptography
This paper is a guide for the pure mathematician who would like to know more
about cryptography based on group theory. The paper gives a brief overview of
the subject, and provides pointers to good textbooks, key research papers and
recent survey papers in the area.Comment: 25 pages References updated, and a few extra references added. Minor
typographical changes. To appear in Proceedings of Groups St Andrews 2009 in
Bath, U
Confidential Boosting with Random Linear Classifiers for Outsourced User-generated Data
User-generated data is crucial to predictive modeling in many applications.
With a web/mobile/wearable interface, a data owner can continuously record data
generated by distributed users and build various predictive models from the
data to improve their operations, services, and revenue. Due to the large size
and evolving nature of users data, data owners may rely on public cloud service
providers (Cloud) for storage and computation scalability. Exposing sensitive
user-generated data and advanced analytic models to Cloud raises privacy
concerns. We present a confidential learning framework, SecureBoost, for data
owners that want to learn predictive models from aggregated user-generated data
but offload the storage and computational burden to Cloud without having to
worry about protecting the sensitive data. SecureBoost allows users to submit
encrypted or randomly masked data to designated Cloud directly. Our framework
utilizes random linear classifiers (RLCs) as the base classifiers in the
boosting framework to dramatically simplify the design of the proposed
confidential boosting protocols, yet still preserve the model quality. A
Cryptographic Service Provider (CSP) is used to assist the Cloud's processing,
reducing the complexity of the protocol constructions. We present two
constructions of SecureBoost: HE+GC and SecSh+GC, using combinations of
homomorphic encryption, garbled circuits, and random masking to achieve both
security and efficiency. For a boosted model, Cloud learns only the RLCs and
the CSP learns only the weights of the RLCs. Finally, the data owner collects
the two parts to get the complete model. We conduct extensive experiments to
understand the quality of the RLC-based boosting and the cost distribution of
the constructions. Our results show that SecureBoost can efficiently learn
high-quality boosting models from protected user-generated data
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