Privacy-Preserving Framework to Facilitate Shared Data Access for Wearable Devices

In order to implement privacy-preserving, efficient and secure data storage and access environment of cloud storage, the following problems must be considered: data index structure, generation and management of keys, data retrieval, treatments of change of users? access right and dynamic operations on data, and interactions among participants. To solve those problems, the interactive protocol among participants is introduced, an extirpation-based key derivation algorithm (EKDA) is designed to manage the keys, a double hashed and weighted Bloom Filter (DWBF) is proposed to retrieve the encrypted keywords, which are combined with lazy revocation, multi-tree structure, asymmetric and symmetric encryptions, which form a privacypreserving, efficient and secure framework for cloud storage. The experiment and security analysis show that EKDA can reduce the communication and storage overheads efficiently, DWBF supports ciphertext retrieval and can reduce communication, storage and computation overhead as well, and the proposed framework is privacy preserving while supporting data access efficiently.

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Data Broadcasting in a Mobile Environment

Wireless Information Highways ◽

10.4018/978-1-59140-568-9.ch004 ◽

2011 ◽

pp. 96-154 ◽

Cited By ~ 5

Author(s):

A.R. Hurson ◽

Y. Jiao

Keyword(s):

Wireless Communication ◽

Mobile Devices ◽

High Performance ◽

Data Access ◽

Data Retrieval ◽

Mobile Environment ◽

Retrieval Method ◽

Shared Data ◽

Communication Techniques ◽

Public Data

The advances in mobile devices and wireless communication techniques have enabled anywhere, anytime data access. Data being accessed can be categorized into three classes: private data, shared data, and public data. Private and shared data are usually accessed through on-demand-based approaches, while public data can be most effectively disseminated using broadcasting. In the mobile computing environment, the characteristics of mobile devices and limitations of wireless communication technology pose challenges on broadcasting strategy as well as data-retrieval method designs. Major research issues include indexing scheme, broadcasting over single and parallel channels, data distribution and replication strategy, conflict resolution, and data retrieval method. In this chapter, we investigate solutions proposed for these issues. High performance and low power consumption are the two main objectives of the proposed schemes. Comprehensive simulation results are used to demonstrate the effectiveness of each solution and compare different approaches.

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A Fine-Grained User-Divided Privacy-Preserving Access Control Protocol in Smart Watch

Sensors ◽

10.3390/s19092109 ◽

2019 ◽

Vol 19 (9) ◽

pp. 2109

Author(s):

Liming Fang ◽

Minghui Li ◽

Lu Zhou ◽

Hanyi Zhang ◽

Chunpeng Ge

Keyword(s):

Access Control ◽

Data Privacy ◽

Privacy Preservation ◽

Data Access ◽

Privacy Preserving ◽

Security And Privacy ◽

Fine Grained ◽

Data Access Control ◽

Smart Watch ◽

Performance And Evaluation

A smart watch is a kind of emerging wearable device in the Internet of Things. The security and privacy problems are the main obstacles that hinder the wide deployment of smart watches. Existing security mechanisms do not achieve a balance between the privacy-preserving and data access control. In this paper, we propose a fine-grained privacy-preserving access control architecture for smart watches (FPAS). In FPAS, we leverage the identity-based authentication scheme to protect the devices from malicious connection and policy-based access control for data privacy preservation. The core policy of FPAS is two-fold: (1) utilizing a homomorphic and re-encrypted scheme to ensure that the ciphertext information can be correctly calculated; (2) dividing the data requester by different attributes to avoid unauthorized access. We present a concrete scheme based on the above prototype and analyze the security of the FPAS. The performance and evaluation demonstrate that the FPAS scheme is efficient, practical, and extensible.

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A Privacy Preserving Cloud-Based K-NN Search Scheme with Lightweight User Loads

Computers ◽

10.3390/computers9010001 ◽

2020 ◽

Vol 9 (1) ◽

pp. 1 ◽

Cited By ~ 1

Author(s):

Yeong-Cherng Hsu ◽

Chih-Hsin Hsueh ◽

Ja-Ling Wu

Keyword(s):

Data Privacy ◽

Nearest Neighbor ◽

Search Algorithm ◽

Data Access ◽

Privacy Preserving ◽

Secret Key ◽

K Nearest Neighbor ◽

Sensitive Data ◽

Cloud Data ◽

Cloud Server

With the growing popularity of cloud computing, it is convenient for data owners to outsource their data to a cloud server. By utilizing the massive storage and computational resources in cloud, data owners can also provide a platform for users to make query requests. However, due to the privacy concerns, sensitive data should be encrypted before outsourcing. In this work, a novel privacy preserving K-nearest neighbor (K-NN) search scheme over the encrypted outsourced cloud dataset is proposed. The problem is about letting the cloud server find K nearest points with respect to an encrypted query on the encrypted dataset, which was outsourced by data owners, and return the searched results to the querying user. Comparing with other existing methods, our approach leverages the resources of the cloud more by shifting most of the required computational loads, from data owners and query users, to the cloud server. In addition, there is no need for data owners to share their secret key with others. In a nutshell, in the proposed scheme, data points and user queries are encrypted attribute-wise and the entire search algorithm is performed in the encrypted domain; therefore, our approach not only preserves the data privacy and query privacy but also hides the data access pattern from the cloud server. Moreover, by using a tree structure, the proposed scheme could accomplish query requests in sub-liner time, according to our performance analysis. Finally, experimental results demonstrate the practicability and the efficiency of our method.

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GPU code generation for ODE-based applications with phased shared-data access patterns

ACM Transactions on Architecture and Code Optimization ◽

10.1145/2541228.2555311 ◽

2013 ◽

Vol 10 (4) ◽

pp. 1-19

Author(s):

Andrei Hagiescu ◽

Bing Liu ◽

R. Ramanathan ◽

Sucheendra K. Palaniappan ◽

Zheng Cui ◽

...

Keyword(s):

Code Generation ◽

Data Access ◽

Shared Data ◽

Data Access Patterns ◽

Access Patterns

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The CDGP Repository for Geothermal Data

10.5194/egusphere-egu2020-7534 ◽

2020 ◽

Author(s):

Mathieu Turlure ◽

Marc Schaming ◽

Alice Fremand ◽

Marc Grunberg ◽

Jean Schmittbuhl

Keyword(s):

Data Access ◽

Pilot Project ◽

Data Services ◽

Shared Data ◽

Use Of Data ◽

Data Formats ◽

Distribution Rules ◽

Deep Geothermal Energy ◽

Project Data ◽

Geothermal Plant

The CDGP Repository for Geothermal DataThe Data Center for Deep Geothermal Energy (CDGP &#8211; Centre de Donn&#233;es de G&#233;othermie Profonde, https://cdgp.u-strasbg.fr) was launched in 2016 by the LabEx G-EAU-THERMIE PROFONDE (http://labex-geothermie.unistra.fr) to preserve, archive and distribute data acquired on geothermal sites in Alsace. Since the beginning of the project, specific procedures are followed to respect international requirements for data management. In particular, FAIR recommendations are used to distribute Findable, Accessible, Interoperable and Reusable data.Data currently available on the CDGP mainly consist of seismological and hydraulic data acquired at the Soultz-sous-For&#234;ts geothermal plant pilot project. Data on the website are gathered in episodes. Episodes 1994, 1995, 1996, and 2010 from Soultz-sous-For&#234;ts have been recently added to the episodes already available on the CDGP (1988, 1991, 1993, 2000, 2003, 2004 and 2005). All data are described with metadata and interoperability is promoted with use of open or community-shared data formats: SEED, csv, pdf, etc. Episodes have DOIs.To secure Intellectual Property Rights (IPR) set by data providers that partly come from Industry, an Authentication, Authorization and Accounting Infrastructure (AAAI) grants data access depending to distribution rules and user&#8217;s affiliation (i.e. academic, industrial, &#8230;).The CDGP is also a local node for the European Plate Observing System (EPOS) Anthropogenic Hazards platform (https://tcs.ah-epos.eu). The platform provides an environment and facilities (data, services, software) for research onto anthropogenic hazards, especially related to the exploration and exploitation of geo-resources. Some episodes from Soultz-sous-For&#234;ts are already available and the missing-ones will be soon on the platform.The next step for the CDGP is first to complete data from Soultz-sous-For&#234;ts. Some data are still missing and must be recovered from the industrial partners. Then, data from the other geothermal sites in Alsace (Rittershoffen, Illkirch, Vendenheim) need to be collected in order to be distributed. Finally, with other French data centers, we are on track to apply the CoreTrustSeal certification (ANR Cedre).The preservation of data can be very challenging and time-consuming. We had to deal with obsolete tapes and formats, even incomplete data. Old data are frequently not well documented and the identification of owner is sometimes difficult. However, the hard work to retrieve, collect old geothermal data and make them FAIR is necessary for new analysis and the valorization of these patrimonial data. The re-use of data (e.g. Cauchie et al, 2020) demonstrates the importance of the CDGP.

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