35,058 research outputs found
Private Data System Enabling Self-Sovereign Storage Managed by Executable Choreographies
With the increased use of Internet, governments and large companies store and
share massive amounts of personal data in such a way that leaves no space for
transparency. When a user needs to achieve a simple task like applying for
college or a driving license, he needs to visit a lot of institutions and
organizations, thus leaving a lot of private data in many places. The same
happens when using the Internet. These privacy issues raised by the centralized
architectures along with the recent developments in the area of serverless
applications demand a decentralized private data layer under user control. We
introduce the Private Data System (PDS), a distributed approach which enables
self-sovereign storage and sharing of private data. The system is composed of
nodes spread across the entire Internet managing local key-value databases. The
communication between nodes is achieved through executable choreographies,
which are capable of preventing information leakage when executing across
different organizations with different regulations in place. The user has full
control over his private data and is able to share and revoke access to
organizations at any time. Even more, the updates are propagated instantly to
all the parties which have access to the data thanks to the system design.
Specifically, the processing organizations may retrieve and process the shared
information, but are not allowed under any circumstances to store it on long
term. PDS offers an alternative to systems that aim to ensure self-sovereignty
of specific types of data through blockchain inspired techniques but face
various problems, such as low performance. Both approaches propose a
distributed database, but with different characteristics. While the
blockchain-based systems are built to solve consensus problems, PDS's purpose
is to solve the self-sovereignty aspects raised by the privacy laws, rules and
principles.Comment: DAIS 201
Entangled cloud storage
Entangled cloud storage (Aspnes et al., ESORICS 2004) enables a set of clients to “entangle” their files into a single clew to be stored by a (potentially malicious) cloud provider. The entanglement makes it impossible to modify or delete significant part of the clew without affecting all files encoded in the clew. A clew keeps the files in it private but still lets each client recover his own data by interacting with the cloud provider; no cooperation from other clients is needed. At the same time, the cloud provider is discouraged from altering or overwriting any significant part of the clew as this will imply that none of the clients can recover their files. We put forward the first simulation-based security definition for entangled cloud storage, in the framework of universal composability (Canetti, 2001). We then construct a protocol satisfying our security definition, relying on an entangled encoding scheme based on privacy-preserving polynomial interpolation; entangled encodings were originally proposed by Aspnes et al. as useful tools for the purpose of data entanglement. As a contribution of independent interest we revisit the security notions for entangled encodings, putting forward stronger definitions than previous work (that for instance did not consider collusion between clients and the cloud provider). Protocols for entangled cloud storage find application in the cloud setting, where clients store their files on a remote server and need to be ensured that the cloud provider will not modify or delete their data illegitimately. Current solutions, e.g., based on Provable Data Possession and Proof of Retrievability, require the server to be challenged regularly to provide evidence that the clients’ files are stored at a given time. Entangled cloud storage provides an alternative approach where any single client operates implicitly on behalf of all others, i.e., as long as one client's files are intact, the entire remote database continues to be safe and unblemishe
Peer-to-peer and community-based markets: A comprehensive review
The advent of more proactive consumers, the so-called "prosumers", with
production and storage capabilities, is empowering the consumers and bringing
new opportunities and challenges to the operation of power systems in a market
environment. Recently, a novel proposal for the design and operation of
electricity markets has emerged: these so-called peer-to-peer (P2P) electricity
markets conceptually allow the prosumers to directly share their electrical
energy and investment. Such P2P markets rely on a consumer-centric and
bottom-up perspective by giving the opportunity to consumers to freely choose
the way they are to source their electric energy. A community can also be
formed by prosumers who want to collaborate, or in terms of operational energy
management. This paper contributes with an overview of these new P2P markets
that starts with the motivation, challenges, market designs moving to the
potential future developments in this field, providing recommendations while
considering a test-case
Smart grid architecture for rural distribution networks: application to a Spanish pilot network
This paper presents a novel architecture for rural distribution grids. This architecture is designed to modernize traditional rural networks into new Smart Grid ones. The architecture tackles innovation actions on both the power plane and the management plane of the system. In the power plane, the architecture focuses on exploiting the synergies between telecommunications and innovative technologies based on power electronics managing low scale electrical storage. In the management plane, a decentralized management system is proposed based on the addition of two new agents assisting the typical Supervisory Control And Data Acquisition (SCADA) system of distribution system operators. Altogether, the proposed architecture enables operators to use more effectively—in an automated and decentralized way—weak rural distribution systems, increasing the capability to integrate new distributed energy resources. This architecture is being implemented in a real Pilot Network located in Spain, in the frame of the European Smart Rural Grid project. The paper also includes a study case showing one of the potentialities of one of the principal technologies developed in the project and underpinning the realization of the new architecture: the so-called Intelligent Distribution Power Router.Postprint (published version
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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