Data Access Scenarios Research Articles

Minimum Repair Bandwidth LDPC Codes for Distributed Storage Systems

In distributed storage systems (DSS), an optimal code design must meet the requirements of efficient local data regeneration in addition to reliable data retention. Recently, low-density parity-check (LDPC) codes have been proposed as a promising candidate that can secure high data rates as well as low repair bandwidth while maintaining low complexity in data reconstruction. The main objective of this study is to optimize the repair bandwidth characteristics of LDPC code families for a DSS application while meeting the data reliability requirements. First, a data access scenario in which nodes contact other available nodes randomly to download data is examined. Later, a minimum-bandwidth protocol is considered in which nodes make their selections based on the degree numbers of check nodes. Through formulating optimization problems for both protocols, a fundamental trade-off between the decoding threshold and the repair bandwidth is established for a given code rate. Finally, conclusions are confirmed by numerical results showing that irregular constructions have a large potential for establishing optimized LDPC code families for DSS applications.

A Legal Framework for Access to Data – A Competition Policy Perspective

The paper strives to systematise the debate on access to data from a competition policy angle. At the outset, two general policy approaches to access to data are distinguished: a “private control of data” approach versus an “open access” approach. We argue that, when it comes to private sector data, the “private control of data” approach is preferable. According to this approach, the “whether” and “how” of data access should generally be left to the market. However, public intervention can be justified by significant market failures. We discuss the presence of such market failures and the policy responses, including, in particular, competition policy responses, with a view to three different data access scenarios: access to data by co-generators of usage data (Scenario 1); requests for access to bundled or aggregated usage data by third parties vis-a-vis a service or product provider who controls such datasets, with the goal to enter complementary markets (Scenario 2); requests by firms to access the large usage data troves of the Big Tech online platforms for innovative purposes (Scenario 3). On this basis we develop recommendations for data access policies.

2Lbp-RRNS: Two-Levels RRNS With Backpropagation for Increased Reliability and Privacy-Preserving of Secure Multi-Clouds Data Storage

Cloud storage as service is the mainstream technology used to retain digital data. However, there are significant risks for confidentiality, integrity, and availability violation associated with the loss of information, denial of access, technical failures, etc. In this article, we propose a two-level 2Lbp-RRNS scheme based on a Redundant Residue Number System with a backpropagation and hamming distance mechanisms for increasing reliability of a configurable and secure multi-cloud data storage. We provide a theoretical analysis of the 2Lbp-RRNS solution as an extension of the classical 2L-RRNS and a variant of fully homomorphic encryption for privacy-preserving, parallel processing, and scalability. We formulate, explain, and prove its main properties to extend existing knowledge within the limits of the critical bounding RRNS assumptions. We show that 2Lbp-RRNS can identify and recover more errors than traditional 2L-RRNS. We provide the upper bounds of the traditional threshold 2L-RRNS and our solution to estimate the number of detectable and correctable errors. We study various data access scenarios and show that it detects 1.58$\times $ and corrects 3.37$\times $ more errors than 2L-RRNS, on average. We also provide efficient implementations of encoding and decoding algorithms MRC8, and MRC16 based on the Mixed-Radix system, Finite Ring Neuronal Network, and signed binary window method. We evaluate encoding/decoding speeds using three algorithms: Mignotte, MRC8, and MRC16. The experimental system includes seven cloud storages: DropBox, GoogleDrive, OneDrive, Sharefile, Box, Egnyte, and Salesforce. To assess the efficiency of the system on real data, we vary scenarios of the first and second levels. The results show that our solution outperforms MRC8 by 2.53$\times $ (1.78$\times $ ), and Mignotte by 4.83$\times $ (11.43$\times $ ) for the encoding (decoding) speed, respectively.

Energy Consumption Between Routing and Bridging Protocol for Eco-Friendly Wireless Networks

The implementation of eco-friendly technology has been become an interesting field for sustainability. No exception with the implementation of wireless technology that used for developing networks infrastructure, it is necessary for saving the usage of energy. As a data forwarding protocol in a computer network, commonly there are two protocols that used for, which are routing and bridging protocol. Technically routing protocol has been confirmed that it is more effective and efficient than bridging protocol. However bridging protocol still becomes the popular protocol for data forwarding because it is easy to use. This research tried to test the energy consumption of the wireless network device that implementing between routing or bridging protocol. The wireless network device that used for this research was MikroTik router RB 433Ah. The data forwarding protocol that was tested consists of bridging, static routing, and RIP routing. Data traffic scenario that used for this research consisted of two scenarios which were HTML data access with packet size 256B and video streaming data access with packet size 1518B. Measuring the energy consumption referred to three parameters which were power consumption, CPU usage, and processor temperature. The result showed that for HTML data access scenario, the RIP routing protocol become the lowest energy consumption with power consumption reached 7.460 W, CPU usage 4.6 %, and processor temperature 38.133^C. While for video streaming scenario, generally the RIP routing protocol still become the lowest energy consumption with power consumption reached 7.567 W, CPU usage 7.33 %, and processor temperature 36.727^C.

Predictive risk modelling under different data access scenarios: who is identified as high risk and for how long?

ObjectiveThis observational study critically explored the performance of different predictive risk models simulating three data access scenarios, comparing: (1) sociodemographic and clinical profiles; (2) consistency in high-risk designation across models;...

Commissioning of a CERN Production and Analysis Facility Based on xrootd

The CERN facility hosts the Tier-0 of the four LHC experiments, but as part of WLCG it also offers a platform for production activities and user analysis. The CERN CASTOR storage technology has been extensively tested and utilized for LHC data recording and exporting to external sites according to experiments computing model. On the other hand, to accommodate Grid data processing activities and, more importantly, chaotic user analysis, it was realized that additional functionality was needed including a different throttling mechanism for file access. This paper will describe the xroot-based CERN production and analysis facility for the ATLAS experiment and in particular the experiment use case and data access scenario, the xrootd redirector setup on top of the CASTOR storage system, the commissioning of the system and real life experience for data processing and data analysis.

Using OWL and SWRL to represent and reason with situation-based access control policies

Access control is a central problem in confidentiality management, in particular in the healthcare domain, where many stakeholders require access to patients' health records. Situation-Based Access Control (SitBAC) is a conceptual model that allows for modeling healthcare scenarios of data-access requests; thus it can be used to formulate data-access policies, where health organizations can specify their regulations involving access to patients' data according to the context of the request. The model's central concept is the Situation, a formal representation of a patient's data-access scenario. In this paper, we present the SitBAC knowledge framework, a formal healthcare-oriented, context-based access-control framework that makes it possible to represent and implement SitBAC as a knowledge model along with an associated inference method, using OWL and SWRL. Within the SitBAC knowledge framework, scenarios of data access are represented as formal Web Ontology language (OWL)-based Situation classes, formulating data-access rule classes. A set of data-access rule classes makes up the organization's data-access policy. An incoming data-access request, represented as an individual of an OWL-based Situation class, is evaluated by the inference method against the data-access policy to produce an ‘approved/denied’ response. The method uses a Description Logics (DL)-reasoner and a Semantic Web Rule Language (SWRL) engine during the inference process. The DL reasoner is used for knowledge classification and for real-time realization of the incoming data-access request as a member of an existing Situation class to infer the appropriate response. The SWRL engine is used to infer new knowledge regarding the incoming data-access requests, which are required for the realization process. We evaluated the ability of the SitBAC knowledge framework to provide correct responses by representing and reasoning with real-life healthcare scenarios.

Situation-Based Access Control: Privacy management via modeling of patient data access scenarios

Access control is a central problem in privacy management. A common practice in controlling access to sensitive data, such as electronic health records (EHRs), is Role-Based Access Control (RBAC). RBAC is limited as it does not account for the circumstances under which access to sensitive data is requested. Following a qualitative study that elicited access scenarios, we used Object-Process Methodology to structure the scenarios and conceive a Situation-Based Access Control (SitBAC) model. SitBAC is a conceptual model, which defines scenarios where patient’s data access is permitted or denied. The main concept underlying this model is the Situation Schema, which is a pattern consisting of the entities Data-Requestor, Patient, EHR, Access Task, Legal-Authorization, and Response, along with their properties and relations. The various data access scenarios are expressed via Situation Instances. While we focus on the medical domain, the model is generic and can be adapted to other domains.

A game theoretic approach to power aware wireless data access

We consider a basic scenario in wireless data access: a number of mobile clients are interested in a set of data items kept at a common server. Each client independently sends requests to inform the server of its desired data items and the server replies with a broadcast channel. We are interested in studying the energy consumption characteristics in such a scenario. First, we define a utility function for quantifying performance. Based on the utility function, we formulate the wireless data access scenario as a noncooperative game - wireless data access (WDA) game. Although our proposed probabilistic data access scheme does not rely on client caching, game theoretical analysis shows that clients do not always need to send requests to the server. Simulation results also indicate that our proposed scheme, compared with a simple always-request one, increases the utility and lifetime of every client while reducing the number of requests sent, with a cost of slightly larger average query delay. We also compare the performance of our proposed scheme with two popular schemes that employ client caching. Our results show that caching-only benefits clients with high query rates at the expense of both shorter lifetime and smaller utility in other clients