Cyber-Resilient Privacy Preservation and Secure Billing Approach for Smart Energy Metering Devices_2

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International Journal of Engineering Trends and Technology Volume 70 Issue 9, 337-345, September 2022

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Original Article

Cyber-Resilient Privacy Preservation and Secure

Billing Approach for Smart Energy Metering Devices

M. Venkatesh Kumar1, C. Lakshmi2

1,2Department of CSE, SRM Institute of Science and Technology, Chennai, Tamilnadu. India.

1Corresponding Author : veenkat@gmail.com

Received: 11 May 2022 Revised: 12 July 2022 Accepted: 20 September 2022 Published: 30 September 2022

Abstract - Most of the smart applications, such as smart energy metering devices, demand strong privacy preservation to

strengthen data privacy. However, it is difficult to protect the privacy of the smart device data, especially on the client side.

It is mainly because payment for billing is computed by the server deployed at the client's side, and it is highly challenging

to prevent the leakage of client's information to unauthorised users. Various researchers have discussed this problem and

have proposed different privacy preservation techniques. Conventional techniques suffer from the problem of high

computational and communication overload on the client side. In addition, the performance of these techniques

deteriorates due to computational complexity and their inability to handle the security of large-scale data. Due to these

limitations, it becomes easy for the attackers to introduce malicious attacks on the server, posing a significant threat to

data security. In this context, this proposal intends to design novel privacy preservation and secure billing framework

using deep learning techniques to ensure data security in smart energy metering devices. This research aims to overcome

the limitations of the existing techniques to achieve robust privacy preservation in smart devices and increase the cyber

resilience of these devices.

Keywords - Cyber Resilient, Data mining, Data Security, Privacy Preservation, Smart Metering.

1. Introduction

Most of the real-time smart applications such as data

sharing, smart grid systems, smart energy devices etc. can

be simulated using a fundamental client-server structure

where the client connects with the server to send requests

and receive requested services from the services (De

Craemer&Deconinck, 2010) [1]. This architecture also

allows the clients to pay for the received services. For

instance, smart devices allow users to contact the

centralised server through their smart gadgets to obtain

relevant knowledge. Applications like smart metering also

follow a basic client-server system wherein smart meters

are deployed on the user's side to collect the electricity

usage data (Arun & Mohit, 2016) [2]. This data will be

further communicated to the electrical companies via

servers (deployed on the company's side). This process of

data sharing is more vulnerable to cyber attacks since the

data sent from the client to the server can expose sensitive

user data, such as the user's private information, to

unauthorised entities (Cheng et al., 2018) [3]. The

attackers can exploit sensitive information such as the

user's identity and financial data to find out the preferences

of the users (Chim et al., 2012) [4] (Li et al., 2015) [5]

(Xing et al., 2017) [6]. Readings from smart meters may be

analysed to determine whether or not a user is there, and

this information can be dangerous. (Garcia &Jacobs, 2010)

[7] Since they can trigger security and safety concerns of

the users. In such cases, privacy preservation becomes a

crucial tool in smart metering devices. In addition, the

payment made by the client (electricity bill) to the energy

companies via a server is also exposed to cyber-attacks.

Hence it is essential to maintain the client's privacy and

secure the billing information using privacy preservation

techniques. Conventional cryptographic techniques were

used in various research works for privacy preservation in

smart energy metering devices (Li et al., 2014) [8] (Yao et

al., 2019) [9] (Syed et al., 2020) [10]. However, these

techniques suffer from certain problems which restrict

their adaptability. These techniques suffer from high

computational overhead and are not feasible for smart

applications (Paulet et al., 2013) [11] (Huang et al., 2014)

[12] (Chen et al., 2019) [13]. Also, it is highly difficult to

hide clients' information from unauthorised users when

they communicate through the servers since the requests

and payments made by the clients follow a traditional

billing process.

In this process, the server computes the bills based on

the utilised service, and the information about the payment

made by the client is easily accessible. It increases security

problems (Molina-Markham et al., 2012) [14] (Alhothaily

et al., 2017) [15]. Most of the existing privacy preservation

techniques for smart metering (Erkin et al., 2013) [16]

(Shen et al., 2017) [36] (Wang, 2017) [18] could not meet

the desired requirements for privacy preservation since

they compromise on the service quality for privacy

preservation.

M. Venkatesh Kumar & C. Lakshmi / IJETT, 70(9), 337-345, 2022

338

Fig. 1 Smart Metering Architecture

In this context, there is a great demand for cyber-

resilient systems that protect smart devices from

adversarial cyber-attacks. In general, cyber-resilient

systems are the systems which can predict, withstand and

recover from adverse cyber-attacks. Since smart energy

metering devices are more susceptible to cyber-attacks,

making these systems cyber-resilient is essential to protect

them from being exploited by attackers. With the

advancement of machine learning and deep learning

techniques, they are used in smart metering devices for

privacy preservation. This research proposes a novel deep

learning-based privacy preservation approach which is

cyber-resilient for secure billing in smart energy metering

devices. The research methodology section discusses a

brief description of the proposed approach.

The Organization of the paper is as follows: Section 2

Security vs Privacy, Section 3 Cyber resilience and

Privacy Preservation, Section 4 Literature survey, Section

5 Problem statement, Section 6 Aim and objective, Section

7 Proposed research methodology, Section 8 Experimental

results, Section 9 Discussion and future research and

Section 10 Conclusion.

2. Security Vs Privacy

Privacy and security are prioritised, protecting data

against misuse and leakage during the data mining process.

The main difference is security deals with safeguarding,

and privacy deals with safeguarding the user's identity.

Security

Privacy

Confidentiality,

availability, and

integrity are referred to

as data security.

Related to the appropriate

use of data.

Controlling of

unauthorised or

modification of

information during the

mining process.

Preventing or not

disclosing individual or

group-related sensitive

information.

3. Cyber Resilience and Privacy Preservation

Cyber resilience is defined as the ability of an

organisation to enable business acceleration by preparing

for, responding to and recovering from threats, i.e. cyber

threats. Cyber resilience is very important because

traditional security is not enough to ensure adequate

security like information security, data security and

network security. The difference between Cyber security

and Cyber resilience is as follows:

Cyber Security

Cyber resilience

Refers to the methods and

processes of protecting

electronic data, which

includes identifying data,

technology, location and

protection.

Refers to withstanding and

recovering from threats

which disrupt business

operations.

3.1. Privacy Preservation

The different models for privacy-preserving were (i).

Trust third-party model, (ii). Semi-honest model, (iii).

Malicious model, and (iv). Other models. In the third-party

trust model, the third party performs the computation. It

delivers the results by following secure protocols, whereas,

in a semi-honest model, inputs were used according to the

protocols. There are no protocols or restrictions on the

participants in the malicious model; in other models,

cryptographic techniques were carried out for performing

data mining tasks.

4. Literature Survey

Privacy preservation in smart metering devices has

gained huge attention among researchers in recent times

(Rial &Danezis, 2011) [19] (Souri et al., 2014) [20]

(Mustafa et al., 2015) [21] (Gohar et al., 2019) [37]. The

emergence of deep learning techniques and privacy

preservation in smart metering has witnessed a significant

transformation. Various researchers have proposed deep

learning-based techniques for protecting sensitive data and

users' privacy from cyber-attacks. (Joudaki et al., 2020)

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InternationalJournalofEngineeringTrendsandTechnologyVolume70Issue9,337-345,September2022ISSN:2231–5381/https://doi.org/10.14445/22315381/IJETT-V70I9P233©2022SeventhSenseResearchGroup®ThisisanopenaccessarticleundertheCCBY-NC-NDlicense(http://creativecommons.org/licenses/by-nc-nd/4.0/)OriginalArticleC...

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