scholarly journals Framework Integrating Lossy Compression and Perturbation for the Case of Smart Meter Privacy

Electronics ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 465 ◽  
Author(s):  
Maik Plenz ◽  
Chaoyu Dong ◽  
Florian Grumm ◽  
Marc Florian Meyer ◽  
Marc Schumann ◽  
...  
Keyword(s):  
Smart Grids ◽  
Relevant Information ◽  
Lossy Compression ◽  
Cyber Attacks ◽  
Perturbation Approach ◽  
Smart Meter ◽  
End User ◽  
Classification Technique ◽  
Private Data ◽  
The One

The encoding of high-resolution energy profile datasets from end-users generated by smart electricity meters while maintaining the fidelity of relevant information seems to be one of the backbones of smart electrical markets. In the end-user sphere of smart grids, specific load curves of households can easily be utilized to aggregate detailed information about customer’s daily activities, which would be attractive for cyber attacks. Based on a dataset measured by a smart meter installed in a German household, this paper integrates two complementary approaches to encrypt load profile datasets. On the one hand, the paper explains an integration of a lossy compression and classification technique, which is usable for individual energy consumption profiles of households. On the other hand, a perturbation approach with the Gaussian distribution is used to enhance the safety of a large amount of privacy profiles. By this complete workflow, involving the compression and perturbation, the developed framework sufficiently cut off the chance of de-noising attacks on private data and implement an additional, easy-to-handle layer of data security.

scholarly journals Data falsification attacks in advanced metering infrastructure

2021 ◽  
Vol 10 (1) ◽  
pp. 412-418
Author(s):  
Hasventhran Baskaran ◽  
Abbas M. Al-Ghaili ◽  
Zul- Azri Ibrahim ◽  
Fiza Abdul Rahim ◽  
Saravanan Muthaiyah ◽  
...  
Keyword(s):  
Smart Grid ◽  
Power Systems ◽  
Smart Grids ◽  
Supply And Demand ◽  
Effective Control ◽  
Smart Meter ◽  
Advanced Metering Infrastructure ◽  
End User ◽  
Smart Meters ◽  
Advanced Metering

Smart grids are the cutting-edge electric power systems that make use of the latest digital communication technologies to supply end-user electricity, but with more effective control and can completely fill end user supply and demand. Advanced Metering Infrastructure (AMI), the backbone of smart grids, can be used to provide a range of power applications and services based on AMI data. The increased deployment of smart meters and AMI have attracted attackers to exploit smart grid vulnerabilities and try to take advantage of the AMI and smart meter’s weakness. One of the possible major attacks in the AMI environment is False Data Injection Attack (FDIA). FDIA will try to manipulate the user’s electric consumption by falsified the data supplied by the smart meter value in a smart grid system using additive and deductive attack methods to cause loss to both customers and utility providers. This paper will explore two possible attacks, the additive and deductive data falsification attack and illustrate the taxonomy of attack behaviors that results in additive and deductive attacks. This paper contributes to real smart meter datasets in order to come up with a financial impact to both energy provider and end-user.

scholarly journals Personality and Dementia: an Approach to Differential Profile of the Caregiver

2011 ◽  
Vol 1 (3) ◽  
pp. 85-94
Author(s):  
Elena de Andrés-Jiménez ◽  
Rosa Mª Limiñana-Gras ◽  
Encarna Fernández-Ros
Keyword(s):  
Relevant Information ◽  
Personality Profile ◽  
Family Carers ◽  
Psychological Variables ◽  
People With Dementia ◽  
The Family ◽  
Study Population ◽  
The One ◽  
The Impact

The aim of this study is to determine the existence of a characteristic personality profile of family carers of people with dementia. The correct knowledge and use of psychological variables which affect the carer, helps to promote appropriate actions to mitigate the impact of care and improve the carer’s quality of life and likewise the one of the person cared for. The study population consists of 69 family carers of people with dementia, members of various associations and care centers. The results allow us to identify a characteristic personality profile for these carers and it reveals a specific psychological working in this sample, although we cannot directly relate it with the tasks of caring for people with this disease, this profile gives us very relevant information to pay more attention to the needs of this group. Moreover, the analysis of personality styles depends on the sex of the family carer, showing, once again, that the woman is in a situation of most vulnerability.

Sustainable Security for the Internet of Things Using Artificial Intelligence Architectures

2021 ◽  
Vol 21 (3) ◽  
pp. 1-22
Author(s):  
Celestine Iwendi ◽  
Saif Ur Rehman ◽  
Abdul Rehman Javed ◽  
Suleman Khan ◽  
Gautam Srivastava
Keyword(s):  
Internet Of Things ◽  
Detection System ◽  
Experimental Testing ◽  
Cyber Attacks ◽  
Internet Technology ◽  
The Internet ◽  
Business Operations ◽  
Private Data ◽  
The Internet Of Things ◽  
Better Than

In this digital age, human dependency on technology in various fields has been increasing tremendously. Torrential amounts of different electronic products are being manufactured daily for everyday use. With this advancement in the world of Internet technology, cybersecurity of software and hardware systems are now prerequisites for major business’ operations. Every technology on the market has multiple vulnerabilities that are exploited by hackers and cyber-criminals daily to manipulate data sometimes for malicious purposes. In any system, the Intrusion Detection System (IDS) is a fundamental component for ensuring the security of devices from digital attacks. Recognition of new developing digital threats is getting harder for existing IDS. Furthermore, advanced frameworks are required for IDS to function both efficiently and effectively. The commonly observed cyber-attacks in the business domain include minor attacks used for stealing private data. This article presents a deep learning methodology for detecting cyber-attacks on the Internet of Things using a Long Short Term Networks classifier. Our extensive experimental testing show an Accuracy of 99.09%, F1-score of 99.46%, and Recall of 99.51%, respectively. A detailed metric representing our results in tabular form was used to compare how our model was better than other state-of-the-art models in detecting cyber-attacks with proficiency.

OP457 A Collaborative Horizon Scanning Alert For Disinvestment And Early Awareness

2020 ◽  
Vol 36 (S1) ◽  
pp. 10-10
Author(s):  
Vigdis Lauvrak ◽  
Kelly Farrah ◽  
Rosmin Esmail ◽  
Anna Lien Espeland ◽  
Elisabet Hafstad ◽  
...  
Keyword(s):  
Information Retrieval ◽  
Interest Group ◽  
Pilot Project ◽  
Relevant Information ◽  
Horizon Scanning ◽  
Google Alerts ◽  
One Year ◽  
The One ◽  
New Research ◽  
Google Search

IntroductionIn 2019, the Norwegian Institute for Public Health and Canadian Agency for Drugs and Technologies in Health (CADTH) received support from HTAi to produce a quarterly current awareness alert for the HTAi Disinvestment and Early Awareness Interest Group in collaboration with the HTAi Information Retrieval Interest Group. The alert focuses on methods and topical issues, and broader forecasts of potentially disruptive technologies that may be of interest to those involved in horizon scanning and disinvestment initiatives in health technology assessment (HTA).MethodsInformation specialists at both agencies developed search strategies for disinvestment and for horizon scanning in PubMed and Google. The template for the alert was based on an e-newsletter developed by the Information Retrieval Interest Group. Information specialists and researchers reviewed the monthly (PubMed) and weekly (Google) search results and selected potentially relevant publications. Additional sources were also identified through regular HTA and horizon scanning work.ResultsAlerts are posted quarterly on the HTAi Interest Group website; members receive an email notice when new alerts are available. While the revised PubMed searches are identifying relevant information, Google alerts have been disappointing, and this search may need to be revised further or dropped. When the one-year pilot project ends, in Fall 2020, interest group members will be surveyed to see if the alerts were useful, and whether they have suggestions for improving them.ConclusionsCollaborating on this alert service reduces duplication of effort between agencies, and makes new research in horizon scanning and disinvestment more accessible to colleagues in other agencies working in these areas.

scholarly journals Modelling End-User Behavior and Behavioral Change in Smart Grids. An Application of the Model of Frame Selection

Energies ◽  
2020 ◽  
Vol 13 (24) ◽  
pp. 6674
Author(s):  
Sebastian Hoffmann ◽  
Fabian Adelt ◽  
Johannes Weyer
Keyword(s):  
Smart Grids ◽  
Online Survey ◽  
End Users ◽  
Influential Factors ◽  
System Stability ◽  
End User ◽  
Frame Selection ◽  
Model Of Frame Selection ◽  
Modes Of Governance ◽  
Strong Control

This paper presents an agent-based model (ABM) for residential end-users, which is part of a larger, interdisciplinary co-simulation framework that helps to investigate the performance of future power distribution grids (i.e., smart grid scenarios). Different modes of governance (strong, soft and self-organization) as well as end-users’ heterogeneous behavior represent key influential factors. Feedback was implemented as a measure to foster grid-beneficial behavior, which encompasses a range of monetary and non-monetary incentives (e.g., via social comparison). The model of frame selection (MFS) serves as theoretical background for modelling end-users’ decision-making. Additionally, we conducted an online survey to ground the end-user sub-model on empirical data. Despite these empirical and theoretical foundations, the model presented should be viewed as a conceptual framework, which requires further data collection. Using an example scenario, representing a lowly populated residential area (167 households) with a high share of photovoltaic systems (30%), different modes of governance were compared with regard to their suitability for improving system stability (measured in cumulated load). Both soft and strong control were able to decrease overall fluctuations as well as the mean cumulated load (by approx. 10%, based on weekly observation). However, we argue that soft control could be sufficient and more societally desirable.

scholarly journals Machine learning for psychiatric patient triaging: an investigation of cascading classifiers

2018 ◽  
Vol 25 (11) ◽  
pp. 1481-1487 ◽  
Author(s):  
Vivek Kumar Singh ◽  
Utkarsh Shrivastava ◽  
Lina Bouayad ◽  
Balaji Padmanabhan ◽  
Anna Ialynytchev ◽  
...  
Keyword(s):  
Machine Learning ◽  
Psychiatric Patient ◽  
Classification Accuracy ◽  
Psychiatric Patients ◽  
Patient Records ◽  
Classification Technique ◽  
Machine Learning Approach ◽  
The One ◽  
Unique Dataset

Abstract Objective Develop an approach, One-class-at-a-time, for triaging psychiatric patients using machine learning on textual patient records. Our approach aims to automate the triaging process and reduce expert effort while providing high classification reliability. Materials and Methods The One-class-at-a-time approach is a multistage cascading classification technique that achieves higher triage classification accuracy compared to traditional multiclass classifiers through 1) classifying one class at a time (or stage), and 2) identification and application of the highest accuracy classifier at each stage. The approach was evaluated using a unique dataset of 433 psychiatric patient records with a triage class label provided by “I2B2 challenge,” a recent competition in the medical informatics community. Results The One-class-at-a-time cascading classifier outperformed state-of-the-art classification techniques with overall classification accuracy of 77% among 4 classes, exceeding accuracies of existing multiclass classifiers. The approach also enabled highly accurate classification of individual classes—the severe and mild with 85% accuracy, moderate with 64% accuracy, and absent with 60% accuracy. Discussion The triaging of psychiatric cases is a challenging problem due to the lack of clear guidelines and protocols. Our work presents a machine learning approach using psychiatric records for triaging patients based on their severity condition. Conclusion The One-class-at-a-time cascading classifier can be used as a decision aid to reduce triaging effort of physicians and nurses, while providing a unique opportunity to involve experts at each stage to reduce false positive and further improve the system’s accuracy.

scholarly journals A Proposal for End-to-End QoS Provisioning in Software-Defined Networks

2017 ◽  
Vol 7 (4) ◽  
pp. 2261 ◽  
Author(s):  
Francesco Lucrezia ◽  
Guido Marchetto ◽  
Fulvio Risso ◽  
Michele Santuari ◽  
Matteo Gerola
Keyword(s):  
Smart Grids ◽  
Service Level ◽  
End User ◽  
Delay Bound ◽  
Qos Guarantee ◽  
End To End Delay ◽  
Network Controller ◽  
Edge Network ◽  
End To End

This paper describes a framework application for the control plane of a network infrastructure; the objective is to feature end-user applications with the capability of requesting at any time a customised end-to-end Quality-of-Service profile in the context of dynamic Service-Level-Agreements. Our solution targets current and future real-time applications that require tight QoS parameters, such as a guaranteed end-to-end delay bound. These applications include, but are not limited to, health-care, mobility, education, manufacturing, smart grids, gaming and much more. We discuss the issues related to the previous Integrated Service and the reason why the RSVP protocol for guaranteed QoS did not take off. Then we present a new signaling and resource reservation framework based on the cutting-edge network controller ONOS.  Moreover, the presented system foresees the need of considering the edges of the network, where terminal applications are connected to, to be piloted by distinct logically centralised controllers. We discuss a possible inter-domain communication mechanism to achieve the end-to-end QoS guarantee.

scholarly journals Centrally Decentralising? Analysing Key Policies and Pathways in Norway’s Electricity Transitions

2020 ◽  
Vol 8 (3) ◽  
pp. 173-184
Author(s):  
Tor Håkon Jackson Inderberg
Keyword(s):  
Consumer Behaviour ◽  
Driving Factors ◽  
Smart Meter ◽  
Smart Metering ◽  
End User ◽  
Energy Flows ◽  
User Demand ◽  
The Government ◽  
Electricity Systems ◽  
State Of Field

With national electricity systems, ‘transition’ may involve decentralising production and ownership, and digitalising the system. These processes are facilitated by smart metering, ‘prosuming,’ and changes in consumer behaviour. Driving factors may be national steering, or the process can be left to the market. In Norway, the government has opted for tightly steered national coordination of three key areas: national smart-meter implementation (since 2011), prosumer regulation (since 2016), and a national end-user demand flexibility regulation (expected to be adopted in 2020). These regulations influence production patterns, energy flows and grid activities. Drawing on organisational fields theory, this article asks: Why was it decided to adopt these policies centrally? Which actors have had greatest influence on policy outputs? And, finally, what of the possible implications? The regulations, developed in a sector in a state of field crisis, have generally been supported by the relevant actors. The Norwegian case can help to explain incumbent roles and field crisis, as well as nuanced drivers in complex transitions, beyond decarbonisation.

scholarly journals A Mobile-Based Attribute Aggregation Architecture for User-Centric Identity Management

2014 ◽  
pp. 266-287
Author(s):  
Alexandre B. Augusto ◽  
Manuel E. Correia
Keyword(s):  
Identity Management ◽  
Personal Health Records ◽  
The Internet ◽  
Digital Identity ◽  
End User ◽  
Massive Growth ◽  
Private Data ◽  
Real Value ◽  
User Centric ◽  
The Web

The massive growth of the Internet and its services is currently being sustained by the mercantilization of users’ identities and private data. Traditional services on the Web require the user to disclose many unnecessary sensitive identity attributes like bankcards, geographic position, or even personal health records in order to provide a service. In essence, the services are presented as free and constitute a means by which the user is mercantilized, often without realizing the real value of its data to the market. In this chapter the auhors describe OFELIA (Open Federated Environment for Leveraging of Identity and Authorization), a digital identity architecture designed from the ground up to be user centric. OFELIA is an identity/authorization versatile infrastructure that does not depend upon the massive aggregation of users’ identity attributes to offer a highly versatile set of identity services but relies instead on having those attributes distributed among and protected by several otherwise unrelated Attribute Authorities. Only the end user, with his smartphone, knows how to aggregate these scattered Attribute Authorities’ identity attributes back into some useful identifiable and authenticated entity identity that can then be used by Internet services in a secure and interoperable way.

Sign in / Sign up

Export Citation Format

Share Document