23,849 research outputs found
ESPOON: Enforcing Security Policies In Outsourced Environments
Data outsourcing is a growing business model offering services to individuals
and enterprises for processing and storing a huge amount of data. It is not
only economical but also promises higher availability, scalability, and more
effective quality of service than in-house solutions. Despite all its benefits,
data outsourcing raises serious security concerns for preserving data
confidentiality. There are solutions for preserving confidentiality of data
while supporting search on the data stored in outsourced environments. However,
such solutions do not support access policies to regulate access to a
particular subset of the stored data.
For complex user management, large enterprises employ Role-Based Access
Controls (RBAC) models for making access decisions based on the role in which a
user is active in. However, RBAC models cannot be deployed in outsourced
environments as they rely on trusted infrastructure in order to regulate access
to the data. The deployment of RBAC models may reveal private information about
sensitive data they aim to protect. In this paper, we aim at filling this gap
by proposing \textbf{} for enforcing RBAC policies in
outsourced environments. enforces RBAC policies in an
encrypted manner where a curious service provider may learn a very limited
information about RBAC policies. We have implemented
and provided its performance evaluation showing a limited overhead, thus
confirming viability of our approach.Comment: The final version of this paper has been accepted for publication in
Elsevier Computers & Security 2013. arXiv admin note: text overlap with
arXiv:1306.482
ELECTRONIC COMMERCE SECURITY IN THE CONTEXT OF THE MEANS OF PAYMENT DEMATERIALIZATION
Some items regarding electronic commerce, electronic vulnerabilities, electronic means of payment, digital money and electronic micropayments are presented below. Then is presented a method of assessing the quality of applications and e-commerce Web sites. This method is then adapted from the operational point of view, developed and implemented in the study of the electronic micropayment systems’ security, in the purpose of analyzing and evaluating their security in the context of the means of payment dematerialization.e-commerce, micropayment, security, encryption, digital economy, EWAM
ARPA Whitepaper
We propose a secure computation solution for blockchain networks. The
correctness of computation is verifiable even under malicious majority
condition using information-theoretic Message Authentication Code (MAC), and
the privacy is preserved using Secret-Sharing. With state-of-the-art multiparty
computation protocol and a layer2 solution, our privacy-preserving computation
guarantees data security on blockchain, cryptographically, while reducing the
heavy-lifting computation job to a few nodes. This breakthrough has several
implications on the future of decentralized networks. First, secure computation
can be used to support Private Smart Contracts, where consensus is reached
without exposing the information in the public contract. Second, it enables
data to be shared and used in trustless network, without disclosing the raw
data during data-at-use, where data ownership and data usage is safely
separated. Last but not least, computation and verification processes are
separated, which can be perceived as computational sharding, this effectively
makes the transaction processing speed linear to the number of participating
nodes. Our objective is to deploy our secure computation network as an layer2
solution to any blockchain system. Smart Contracts\cite{smartcontract} will be
used as bridge to link the blockchain and computation networks. Additionally,
they will be used as verifier to ensure that outsourced computation is
completed correctly. In order to achieve this, we first develop a general MPC
network with advanced features, such as: 1) Secure Computation, 2) Off-chain
Computation, 3) Verifiable Computation, and 4)Support dApps' needs like
privacy-preserving data exchange
GraphSE: An Encrypted Graph Database for Privacy-Preserving Social Search
In this paper, we propose GraphSE, an encrypted graph database for online
social network services to address massive data breaches. GraphSE preserves
the functionality of social search, a key enabler for quality social network
services, where social search queries are conducted on a large-scale social
graph and meanwhile perform set and computational operations on user-generated
contents. To enable efficient privacy-preserving social search, GraphSE
provides an encrypted structural data model to facilitate parallel and
encrypted graph data access. It is also designed to decompose complex social
search queries into atomic operations and realise them via interchangeable
protocols in a fast and scalable manner. We build GraphSE with various
queries supported in the Facebook graph search engine and implement a
full-fledged prototype. Extensive evaluations on Azure Cloud demonstrate that
GraphSE is practical for querying a social graph with a million of users.Comment: This is the full version of our AsiaCCS paper "GraphSE: An
Encrypted Graph Database for Privacy-Preserving Social Search". It includes
the security proof of the proposed scheme. If you want to cite our work,
please cite the conference version of i
ODIN: Obfuscation-based privacy-preserving consensus algorithm for Decentralized Information fusion in smart device Networks
The large spread of sensors and smart devices in urban infrastructures are motivating research in the area of the Internet of Things (IoT) to develop new services and improve citizens’ quality of life. Sensors and smart devices generate large amounts of measurement data from sensing the environment, which is used to enable services such as control of power consumption or traffic density. To deal with such a large amount of information and provide accurate measurements, service providers can adopt information fusion, which given the decentralized nature of urban deployments can be performed by means of consensus algorithms. These algorithms allow distributed agents to (iteratively) compute linear functions on the exchanged data, and take decisions based on the outcome, without the need for the support of a central entity. However, the use of consensus algorithms raises several security concerns, especially when private or security critical information is involved in the computation.
In this article we propose ODIN, a novel algorithm allowing information fusion over encrypted data. ODIN is a privacy-preserving extension of the popular consensus gossip algorithm, which prevents distributed agents from having direct access to the data while they iteratively reach consensus; agents cannot access even the final consensus value but can only retrieve partial information (e.g., a binary decision). ODIN uses efficient additive obfuscation and proxy re-encryption during the update steps and garbled circuits to make final decisions on the obfuscated consensus. We discuss the security of our proposal and show its practicability and efficiency on real-world resource-constrained devices, developing a prototype implementation for Raspberry Pi devices
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