Advancing Security and Data Protection for Smart Home Systems

through Blockchain Technologies

Ciprian Paduraru, Rares Cristea and Alin Stefanescu

University of Bucharest, Romania

Keywords:

Smart Home, IoT, Blockchain, Services, Security, Hyperledger.

Abstract:

Internet of Things (IoT) systems are becoming ever-present in our lives and the demand recently increased

with the explosion of external services offered by healthcare, smart city or smart home providers. How-

ever, the connection of private IoT-driven smart home systems and passing data to these external services can

pose signiﬁcant privacy issues, such as information theft or attacks to control, monitor, or harm personal re-

sources. In our paper, we address the identiﬁed security issues through a comprehensive architecture based

on blockchain technology, namely the Hyperledger Fabric platform. We underscore the value that a permis-

sioned blockchain brings in addressing performance issues both architecturally and through fog computing,

and propose a pipeline to mitigate known security threats through static and live monitoring techniques.

1 INTRODUCTION

Smart home systems are often deﬁned as a set of in-

terconnected devices in a private home that send and

receive data in real time. Typically, they automate

various household tasks via smart devices such as

TVs, lamps, or fridges. These devices use a dedicated

home communication system between appliances and

other environments, usually wirelessly. Users can ac-

cess these products to monitor, control the device be-

haviour or extract useful information.

Motivation for using a Blockchain Technology.

Due to space constraints, the reader new to the

blockchain ﬁeld is invited to check the basic techni-

cal aspects in the existing literature (Ma et al., 2019),

(Antwi et al., 2021), (Zhang, 2020). Smart home

systems consist of heterogeneous devices made in-

teroperable by creating gateway connections. In the

absence of security standards for their connection, it

is difﬁcult to assess the security of the network and

whether the privacy of the data collected by the de-

vices is vulnerable to attacks. Centralized architec-

tures (gateways) are vulnerable to data forgery, tam-

pering, denial-of-service attacks, etc. An example of

this is the hacking of toddler surveillance cameras

(Schiefer, 2015). From this type of studies, at secu-

rity level, we identiﬁed the following requirements:

• Conﬁdentiality: since the networks used in smart

homes could collect and store sensitive informa-

tion, access to this data should be restricted to

authorized individuals. Blockchain is capable of

providing a solution when paired with encryption

algorithms. (Dotan et al., 2021), (Zhang et al.,

2019), (Zou et al., 2020), (Abdelmaboud et al.,

2022).

• Data integrity: Connected devices communicat-

ing with each other must maintain data integrity

and prevent forged information from ﬂowing

through the network.

• Authentication: It is important to prevent attack-

ers from connecting to the network and then act-

ing maliciously.

Contributions of the Paper.

• To the best of the authors’ knowledge, this is

the ﬁrst work that brings together all current

smart home system requirements into a uniﬁed

architecture and framework, while proposing a

blockchain-based solution to mitigate security

threats. APIs and infrastructures for data collec-

tion, processing, and collaboration between third

parties such as marketplaces, service providers,

smart cities, and external collaboration in general,

are also discussed.

• We propose an open-source full-stack system

based on a customizable permissioned blockchain

solution, i.e., Hyperledger Fabric

. Although

technologies change over time, we still consider it

a novelty, as we believe that the fundamental ideas

behind this solution will be useful over time.

https://www.hyperledger.org/use/fabric

492

Paduraru, C., Cristea, R. and Stefanescu, A.

Advancing Security and Data Protection for Smart Home Systems through Blockchain Technologies.

DOI: 10.5220/0011310800003266

In Proceedings of the 17th International Conference on Software Technologies (ICSOFT 2022), pages 492-499

ISBN: 978-989-758-588-3; ISSN: 2184-2833

• We propose a user-customizable fog computing

method that allows homeowners to select and use

their own devices either in their own homes or

controlled cloud resources for advanced comput-

ing activities.

2 RELATED WORK

The work in (Ammi et al., 2021) is closely related to

ours. The purpose is to provide a technical perspec-

tive on how a smart home system having a permis-

sioned blockchain can be deployed on a Hyperledger

Fabric. They use a cloud storage (virtual servers) to

store resources - which we think it is not really suit-

able for real-time computing devices in terms of re-

sponsiveness, as also suggested, for example, by the

work in (Moniruzzaman et al., 2020). Instead, our

solution uses the InterPlanetary File System (IPFS)

framework (Steichen et al., 2018) to store only crypto-

graphic hashes of data addresses in the blocks. Then,

the data may physically exist either in a local stor-

age for critical operations or in the cloud for big data

management. Additionally, their work does not take

into account dynamic user management (e.g., vis-

itors, healthcare providers who might ask for trig-

gers), policy and security management, or integra-

tion to various external systems such as application

marketplaces or data services. (Lee et al., 2020) pro-

poses a blockchain-based solution for smart homes at

the gateway level to mitigate security issues. Their

study is based on a decentralized architecture that

uses Ethereum to support the security requirements

of conﬁdentiality, authentication, and data integrity

in the smart home gateway. Overall, their methods

work by analyzing the data ﬂow at the center of the

smart home gateway, including scenario conﬁgura-

tions and security considerations for various attacks

on the network. In (Lee et al., 2017), the authors

propose a secure ﬁrmware veriﬁcation for embed-

ded devices to prevent ”patch ﬁle forgery” attacks us-

ing a blockchain architecture. Their proposed tech-

nique guarantees that the ﬁrmware on embedded de-

vices is not tampered with while being up-to-date. In

(Dorri et al., 2017), the authors propose a blockchain-

based method to deﬁne an architectural identiﬁcation

management system for IoT devices, the FairAccess

Framework. The novelty of the paper is the solu-

tion they use for identity veriﬁcation when accessing

resources between interconnected devices. The au-

thors use smart contracts to express and verify contex-

tual control policies when making authorization de-

cisions. In (Ouaddah et al., 2017), the authors con-

sider the fact that traditional access control methods

are costly in terms of power consumption and pro-

cessing overhead, which is a problem for embedded

devices. They also believe that public blockchains

are not suitable due to the limited resources of the

devices. As for other implementation platforms and

languages used, the work in (Calo et al., 2018) and its

extension in (Hossain et al., 2020) use Ethereum and

Solidity. They address the use cases of smart home

systems and emergency services. The goal of both pa-

pers is to provide a practical introduction to the use of

Ethereum and smart contracts, without research im-

plications or other contributions. Each IoT device

is registered on the network via the blockchain. As

for the application of Hyperledger Fabric (HF) to our

solution, we were encouraged by the recent applica-

tion of this technology. For example, in (Antwi et al.,

2021), the authors use HF for the healthcare industry.

In (Zhang, 2020) and (Ma et al., 2019), HF is used

as a deployment platform for supply chain ﬁnancial

management. Some of the beneﬁts are the provision

of a certiﬁcate authority and the protection of privacy

and data using the channels concept.

3 SMART HOME SYSTEMS

Having gathered the requirements of smart home sys-

tems from literature and industrial applications, in

this section we try to present the basic details about

blockchain technologies that might be suitable for our

targeted application domain.

3.1 Requirements for the Smart Home

Systems and Blockchain Motivation

The application of centralized access control mecha-

nisms in the IoT domain and the ever-increasing num-

ber of connected devices can affect scalability, trust,

and security management. Given the theory and re-

cent applications of blockchains, we believe that in-

corporating blockchains into the architecture of smart

home ecosystems can help in many ways. First, it can

support management and data exchange between de-

vices in a decentralized manner. This is a kind of pre-

requisite for IoT in general, as the goal is to achieve

scalability and security, which is not possible with

centralized architectures where a single server man-

ages user permissions, performs veriﬁcations, and

makes various queries from the set of interconnected

devices. The data in the blockchain nodes, which can

be used by different devices, descentralized and en-

crypted, can meet the security requirements of conﬁ-

dentiality, integrity, and authentication in smart home

Advancing Security and Data Protection for Smart Home Systems through Blockchain Technologies

493

systems. This eliminates the need for an external in-

termediary, which may be vulnerable to attacks.

3.2 Motivations for a Permissioned

Blockchain and Hyperledger

Different open-source platforms are available and

widely used in literature, such as Ethereum (Buterin,

2013), Hyperledger (Cachin, 2016), and Corda R3

(Polge et al., 2021).

Some of the key requirements for a blockchain-

based smart home environment are to share user data

only with a closed list of entities rather than with the

public and to provide different levels of control for

different types of users. Users can be either humans

or IoT-connected devices within the smart home. This

is only possible with an approved framework such as

Hyperledger or Corda. These two also provide ﬁne-

grained access control, meaning participants can be

restricted via policies and channels for reading, creat-

ing or updating data. In addition, the consensus mech-

anisms in Hyperledger Fabric and Corda can be set up

to include only a subset of participants, and this has

two major implications: (a) user privacy can be main-

tained by an approved list of parties and (b) it can be

executed faster than Ethereum’s Proof of Work (PoW)

consensus mechanism, making it impractical for ap-

plications that require rapid response, such as smart

home systems (Xu et al., 2017), (Polge et al., 2021),

(Krishnapriya and Sarath, 2020). These functions can

meet the conﬁdentiality, scalability, and availability

requirements of smart home systems. In light of the

requirements of the GDPR, which are generally difﬁ-

cult to meet with a blockchain solution, Hyperledger

offers users the right to delete data by creating a trans-

action that simply marks certain data as deleted. This

is an advantage over its peers. This additional feature

and the fact that Corda is more focused on ﬁnancial

services tipped the scales in our ﬁnal decision to use

Hyperledger Fabric as the deployment platform for

the proposed solution. On the other hand, one of the

drawbacks of the platform is that, while trying to be

as open as possible, i.e., allowing users to customize

things like credentials, consensus algorithm, private

data and channels, developers need to put more effort

to properly implement and connect interfaces. There-

fore, there is an engineering price to pay.

4 PROPOSED ARCHITECTURE

This section ﬁrst discusses the basic architectural

principles for connecting smart home systems to

blockchains and how these types of systems can in-

teract with external, i.e., outside their own ecosys-

tem, applications and users. Then, the intricacies of

the different layers that have been shown to help with

computational overhead are presented, as well as the

implementation details of security management and

interactivity methods.

4.1 Key Ideas in Applying Blockchain

Technology for Smart Home

Systems

The basic idea of the underlying blockchain technol-

ogy is that users, who can be either people (owners of

the home ecosystem or external), agents, devices, etc.,

create transactions for the operations they perform.

The transaction is approved using miners and consol-

idated into blocks. The purpose and motivation of a

blockchain is to track the operations performed on the

shared ledger so that privacy and other metrics can

be tracked. For example, an automated agent could

query the ledger to understand if the sequence of op-

erations may potentially lead to a data loss of which

the human user (owner) was unaware. Or the agent

could detect the sequence of operations that were ex-

pensive and resulted in poor battery performance of a

set of embedded devices. At this point, one might ask

what is the difference between a classical method of

logging the operations in a ﬁle and using the overhead

of a blockchain. We answer this with two main argu-

ments: (a) the transactions are validated by consen-

sus, i.e., multiple parties, using a blockchain, which

prevents the possibility of hacking the log ﬁles, (b) the

data is stored in a structured format with timestamps

and sequence of operations, which makes it easy to

track and can be automatically followed by queries.

Our choice for a permissioned blockchain was

also reinforced by arguments given in (Ammi et al.,

2021) and (Moniruzzaman et al., 2020), which we

summarize below:

• Sensitive data should be kept private, i.e., the user

does not have to share their data with the public.

Also, third parties should not be trusted without

the user’s explicit permission.

Transaction latency is an important factor, as

conﬁrmation of transactions in the smart home

use case must occur within seconds, not min-

utes. From our evaluation of the HF, the expected

performance in standard production environments

can exceed 100,000 transactions per second (Xu

et al., 2021). In such environments, data is usu-

ally updated by more than one user.

• Connecting with smart cities, external applica-

tions and services, other identities in identities in

ICSOFT 2022 - 17th International Conference on Software Technologies

494

Figure 1: The four layers architecture and related en-

tities for fulﬁlling the requirements of a modern smart

home ecosystem and its interaction with users, vendors,

blockchain technology, and external services.

general requires the use of blockchain solutions in

the form of communities or consortia. This is the

reason for choosing a permissioned blockchain in-

stead of a private one.

After reviewing the literature and requirements, in

Fig. 1 we present an architecture that connects indi-

vidual smart home systems to common ecosystems

as required by industry and users. In the proposed

architecture, we also architecturally link the current

requirements with blockchain mechanisms. Within a

smart home system, user can be understood as either

people, devices, or AI agents.

The base layer consists of the devices and storage

layers that are present in one’s home. These may be

devices in the areas of entertainment, health, sensors,

etc. The data from these devices can be stored either

locally, in the cloud, or on a decentralized platform

based on blockchain technologies. The permissioned

blockchain platform, which is the second layer, con-

tains three main components.

• One or more Miners in the user’s house and helps

to keep the shared ledger of operations in a good

state, i.e., checking new transactions from the

connected devices and adding them to blocks by

applying the consensus algorithm. As further ex-

plained in our paper, the set of miner devices can

be adjusted by the user by simulating the capabil-

ities of fog computing.

• A framework that enables the use of high-level

blockchain operations and authentication. In our

Table 1: Mapping some of the core blockchain concepts to

smart home use case.

Concept Description Examples

Assets resources and

devices managed

by the blockchain

network

window sen-

sor, thermostat,

smart TV, wire-

less toothbrush,

vacuum cleaner

Participants

/ users

actors who own

the assets or act

on them from

outside

homeowners,

children, visi-

tors, healthcare

providers, data

mining agents

Transactions set of activities

that take place

within the net-

work, managed

by smart con-

tracts (chaincode

in HF terminol-

ogy) that act on

assets or identity

management

change status

of the lights,

raise temperature,

add or reject a

new user, collect

metrics from the

toothbrush, start

or stop vacuum

cleaner

case, we used Hyperledger Fabric, but other sys-

tems such as Ethereum can also be used.

• A list of smart contracts - chaincodes in the ter-

minology of HF - that can be used to implement

business logic using rules and facilitate the de-

centralization of transactions. Speciﬁcally, in our

use case, they deﬁne how devices interact and

exchange data with other applications inside and

outside the user home ecosystem.

4.2 Mapping Smart Home Entities to

Blockchain Terminology

Table 1 presents our proposed mapping of the three

main concepts, i.e., assets, participants, and transac-

tions from the blockchain terminology, and in partic-

ular the Hyperledger Fabric Model, to the use cases

needed by the smart home systems.

Fig. 2 shows the proposed blockchain network in-

tegration for our use cases in smart home systems.

.The organizations in our framework are divided into

three main categories:

1. Internal smart home system organization: group-

ing the managers of the home itself and their as-

sets (devices).

2. Marketplaces and external application providers:

Grouping of external applications and interactions

with which a smart home system might inter-

act (e.g., stores, smart cities, intelligent trafﬁc

management, etc.). Currently, these services are

grouped into a single organization, but in a real-

life environment, these could be expanded and

Advancing Security and Data Protection for Smart Home Systems through Blockchain Technologies

495

split into different organizations.

3. External users, such as visitors or healthcare

providers, who connect to assets in the home to

temporarily use resources or collect metrics.

One of the main ideas to achieve safer and faster

response times in HF technology is to use different

communication channels between groups of entities

and/or organizations. Each of these channels stores

its own ledger and status, can be managed by one or

more organizations, and has its own rules for conﬁrm-

ing activities and membership. Applying this HF con-

cept to smart home systems facilitates the manage-

ment and tracking of communication between groups

with different activities.

Fig. 3 shows the low-level details of how the

blockchain network gets requests approved by the

HF infrastructure. Note that in our proposed frame-

work, administrators have the option to choose differ-

ent computing capacities for processing endorsement

peers (EP), principals (OP), or commit peers (EP). In-

ternal hardware assets such as computers, laptops, cell

phones, smart TVs, etc. (including those that partic-

ipate in the smart home infrastructure as assets) can

be used as physical deployments for these peers. In

addition, a single physical entity can serve multiple

roles, i.e., a computer could be either EP, OP, and CP.

Cloud devices or externally rented devices can also be

assigned as peers. This determination could be made

through the administrators’ UI interface.

Initializing the system from a high-level perspec-

tive within our framework is done as follows. The

conﬁguration of the organization, policies, admin

users, and channels that enable basic communication

and identity management in-house is ﬁrst created by

a conﬁguration script. This information is added to

the block genesis of the blockchain when it is instan-

tiated. Later, when new assets or users are added to

the smart home environment, change requests are re-

ceived by the admin interface. When approved, they

are recorded as transactions and added to the ledger

in new blocks. Organizations and channels can also

be added on the ﬂy. Similarly, requests must be ap-

proved and executed by administrators, resulting in

transactions, and eventually new blocks are added to

the ledger. This is the case, for example, when a

new health monitoring system is added to the smart

home assets, allowing doctors to monitor some of the

embedded devices monitored in the home. In this

way, a new organization for healthcare providers and

channels for communicating with these devices is cre-

ated at runtime. Other examples include applications

downloaded from a marketplace or external services

related to smart city management.

4.3 Managing the Home Ecosystem

Our framework assumes that owners and administra-

tors have a user interface that is accessible from a

computer, smartphone, or voice control device. This

would be the entry point for controlling permissions,

access, and monitoring of devices and privileges of

other users. Also, through the interface, users can

customize the details of the miners that participate as

resources in the operations of the blockchain infras-

tructure. As shown in Fig. 2, communication between

the interface UI and the devices and other external en-

tities within our framework is handled through REST

API, relying on a central gateway that we refer to as

Central Hub in our framework. If needed, multiple

gateways and hubs can be connected hierarchically to

enable fast transmission and communication. The ex-

ternal users connect to the smart home infrastructure

through the central hub using a communication proxy

component.

For data storage, we found that large ﬁles and

data cannot be stored on the blockchain because the

blocks could be bloated with data that needs to be

mined, and this would signiﬁcantly impact the com-

putational and communication overhead. One solu-

tion mentioned in (Steichen et al., 2018) is to use the

InterPlanetary File System (IPFS) framework. IPFS

is a peer-to-peer ﬁle sharing system that could solve

the problem of efﬁciency in storing and sharing large

data. We also adopt this solution and instead of stor-

ing large amounts of data in transactions and blocks,

we only store references and cryptographic hashes of

data in the blocks. A concrete example could be cam-

era sensors in a smart home system that could poten-

tially send large amounts of data on a regular basis for

automatic analysis by various systems. Rather than

copying this type of data deep into a transaction or

block within the blockchain, our proposed solution

stores the data on the camera device itself or on the

device performing the computational analysis by us-

ing IPFS and creating a reference to it, which is then

used as a hash/reference within the blockchain-based

framework. However, some of the local data storage

is backed up to the cloud if the user chooses so. For

example, it is advisable to store the user identity wal-

lets in a database that is backed up in the cloud.

5 EVALUATION

Our application dataset, source code is open sourced

at https://github.com/unibuc-cs/IoT-application-set.

Since the framework is currently only a prototype

that is not deployed in production environments, it

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496

Figure 2: The three main levels of organization we propose: the smart home ecosystem, visitors and facilities that need

to capture certain metrics, smart cities, and vendors. In production, these could be further divided hierarchically into sub-

organizations or grouped into consortia of other organizational levels. Interaction between the smart home organization and

others occurs through channels, each with agreed-upon smart contracts implemented in chaincode and its own ledger of

operations, separated for performance and security reasons. The Certiﬁcation Authority (CA) component is used to recruit

and enroll new users to the systems. Note also that in our framework, new organizations and communication channels can

be added spontaneously along with the smart contracts. For example, a property manager could install a new application that

performs automatic grocery shopping.

is difﬁcult to evaluate real metrics. We follow the

methodology of evaluation in (Lee et al., 2020) and

(Antwi et al., 2021) and present how our framework

addresses common issues related to performance and

security threats instead of simulated metrics.

A. Applications Dataset and Performance Aspects

The current prototype implementation of the frame-

work has an initial collection of ﬁve simulation ap-

plications that are either independent or communi-

cate with each other via a central hub connection: a

smart TV, a smart window and lighting system, an au-

tomatic plant watering system, a smart water heater,

and a wirelessly accessible toothbrush. In the applica-

tion marketplace, we offer two new applications that

can be installed by the user: an entertainment lighting

system and a smart vacuum cleaner connected to the

sensors and cameras of the smart home system. These

applications are created in different languages such as

C++, Rust, or Python. An organization called Den-

talServices can access and monitor the smart tooth-

brush and send notiﬁcations to the interface UI, simu-

lating health services. An automatic payment applica-

tion is externally connected to the system to monitor

the power consumption of the applications.

Users with administrative rights (homeowners)

can granularly select which of the devices in the house

are allowed to participate in mining processes within

the blockchain network, i.e., commits, orders, and en-

dorsements from peers, through the UI interface. Ei-

ther personal cell phones, computers or IoT devices

can be part of these processes. We strongly believe

that this access feature can provide vertical scalabil-

ity (i.e., adding new devices at runtime) for the use

case of smart home environments, as operations can

be further partitioned as needed. Performance can be

monitored live via the UI dashboards by integrating

Splunk

tool, which we describe below.

B. Security Evaluation

To mitigate security threats, we apply protection at

two different levels in our framework:

1. Proactive measures - e.g., automated inspection of

chain codes through static analysis using the work

in (Yamashita et al., 2019).

2. Live monitoring of network performance and se-

curity through automated triggers or visualization

dashboards using Splunk.

As shown below, many of the threads can only be de-

tected by correlating data across the blockchain.

We ﬁrst describe how to respond to common at-

tacks on distributed systems:

• Denial of Service (DoS): can disrupt network

availability by ﬂooding the network with requests,

and is generally difﬁcult to prevent proactively.

https://www.splunk.com

Advancing Security and Data Protection for Smart Home Systems through Blockchain Technologies

497

Figure 3: The processes that take place in an internal smart home organization: New users are registered by requesting access

from an MSP and from an assigned certiﬁcate authority (CA), which creates a certiﬁcate for the new user. When various

requests are received from external organizations either through the admin UI interface or through the smart home’s proxy,

they are ﬁrst converted into transaction proposals. The transactions are then endorsed by the assigned endorsement peers (EP),

timestamped by the ordering peers (OP), and ﬁnally committed to the general ledger by the committing peers (CP).

This risk can be mitigated by capturing two key

performance metrics: transactions throughput and

latency. Within our framework, we propose auto-

mated triggers using Splunk integration that con-

stantly check these metrics and raise alarms at UI

of homeowners when certain thresholds are ex-

ceeded, such as when the number of transactions

from one of the users or applications has increased

by more than twice the number of operations or

the CPU time it takes to resolve.

• Consensus Manipulation: the consensus mecha-

nism can be attacked with classic DoS, but also

with transaction reordering attacks. At the mo-

ment, HF uses only Crash Fault Tolerant (CFT)

consensus algorithms (Liu et al., 2016), so it can-

not detect malicious actors. According to the doc-

umentation of HF, there are plans to add Byzan-

tine Fault Tolerant (BFT) algorithms (Liu et al.,

2016), which should theoretically detect on av-

erage 1/3 of malicious actors. However, at the

moment, the triggers and dashboards provided by

our system and Splunk that query and report lead-

ership election attempts and transaction latencies

are able to detect malicious actors at runtime.

Second, we describe how the framework responds to

common blockchain attacks that we identiﬁed in our

use case at HF:

• Smart Contract Exploitation: may target and af-

fect business logic and/or network performance.

To mitigate this risk before injecting a new or up-

dated chaincode into the system, our framework

has implemented a system checker chaincode that

launches the Hyperledger Lab Chaincode Ana-

lyzer static analysis tool and uses the work in (Ya-

mashita et al., 2019) to detect some of the po-

tential risks. Runtime monitoring uses metrics,

triggers and dashboards similar to those used in

DDoS attack response. For example, alerts can be

automatically triggered if some of the chaincode

calls take much longer than expected or average,

or if the CPU and memory requirements have un-

expectedly increased.

• MSP Compromise: This attack is more of a HF

speciﬁc threat, but its roots could be in the other

frameworks as well. To mitigate this risk, our

proposed system provides automatic triggers and

alerts. For example, it would be suspicious if a

particular user attempted to make a high number

of connections to a channel or application within

a certain time period, resulting in latency and/or

throughput issues.

6 CONCLUSION AND FUTURE

WORK

After in-depth analysis, we conclude that permis-

sioned blockchain has the potential to improve se-

curity and trust in such systems. We also found

that some architectural choices could increase per-

formance, security, and tracking capabilities: con-

ﬁgurable fog-like computing capabilities, use of the

IPFS protocol as much as possible instead of deep-

copying data, and smart organization of users into

groups as well as private communication channels and

ledgers. As future work, we will contact industrial

partners to apply the concepts and our open source

framework. We plan to further develop the connection

between different smart home providers as a consor-

tium and provide an interface proxy API and generic

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498

smart contracts to connect them. As for the security of

smart contracts, we believe that the current state can

be improved through formal veriﬁcation and fuzzing

techniques before deploying new chain code.

ACKNOWLEDGEMENTS

This research was supported by the European Re-

gional Development Fund, Competitiveness Oper-

ational Program 2014-2020 through project IDBC

(code SMIS 2014+: 121512).

REFERENCES

Abdelmaboud, A., Ahmed, A. I. A., Abaker, M., Eisa, T.

A. E., Albasheer, H., Ghorashi, S. A., and Karim, F. K.

(2022). Blockchain for IoT applications: Taxonomy,

platforms, recent advances, challenges and future re-

search directions. Electronics, 11(4).

Ammi, M., Alarabi, S., and Benkhelifa, E. (2021).

Customized blockchain-based architecture for secure

smart home for lightweight IoT. Information Process-

ing & Management, 58:102482.

Antwi, M., Adnane, A., Ahmad, F., Hussain, R., Habib ur

Rehman, M., and Kerrache, C. A. (2021). The case

of HyperLedger Fabric as a blockchain solution for

healthcare applications. Blockchain: Research and

Applications, 2(1):100012.

Buterin, V. (2013). Ethereum white paper: A next gener-

ation smart contract & decentralized application plat-

form. Technical report, Ethereum Foundation.

Cachin, C. (2016). Architecture of the Hyperledger

blockchain fabric. In Workshop on distributed

cryptocurrencies and consensus ledgers (DCCL’16),

pages 1–4.

Calo, S., Verma, D., Chakraborty, S., Bertino, E., Lupu, E.,

and Cirincione, G. (2018). Self-generation of access

control policies. In Proc. of SACMAT’18, pages 39—

-47. ACM.

Dorri, A., Kanhere, S. S., Jurdak, R., and Gauravaram, P.

(2017). Blockchain for IoT security and privacy: The

case study of a smart home. In 2nd IEEE PERCOM

Workshop On Security Privacy And Trust In The In-

ternet of Things, pages 618–623. IEEE.

Dotan, M., Pignolet, Y.-A., Schmid, S., Tochner, S., and

Zohar, A. (2021). Survey on blockchain networking:

Context, state-of-the-art, challenges. ACM Comput.

Surv., 54(5).

Hossain, M., Waheed, S., Rahman, Z., and Hossain, M.

(2020). Blockchain for the security of internet of

things: A smart home use case using Ethereum. In-

ternational Journal of Recent Technology and Engi-

neering, 8.

Krishnapriya, S. and Sarath, G. (2020). Securing land reg-

istration using blockchain. Procedia Computer Sci-

ence, 171:1708–1715. 3rd Int. Conf. on Computing

and Network Communications (CoCoNet’19).

Lee, B., Malik, S., Wi, S., and Lee, J.-H. (2017).

Firmware veriﬁcation of embedded devices based on

a blockchain. In Proc. of QShine’16 conference, vol-

ume 199 of LNICST, pages 52–61. Springer.

Lee, Y., Rathore, S., Park, J., and Park, J. (2020). A

blockchain-based smart home gateway architecture

for preventing data forgery. Human-centric Comput-

ing and Information Sciences, 10:9.

Liu, S., Viotti, P., Cachin, C., Qu

ema, V., and Vukolic,

M. (2016). XFT: Practical fault tolerance beyond

crashes. In 12th USENIX Conf. on Operating Systems

Design and Implementation (OSDI’16), pages 485—-

500. USENIX Association.

Ma, C., Kong, X., Lan, Q., and Zhongding, Z. (2019). The

privacy protection mechanism of Hyperledger Fabric

and its application in supply chain ﬁnance. Cyberse-

curity, 2.

Moniruzzaman, M., Khezr, S., Yassine, A., and Benlamri,

R. (2020). Blockchain for smart homes: Review

of current trends and research challenges. Comput.

Electr. Eng., 83:106585.

Ouaddah, A., Elkalam, A., and Ouahman, A. (2017).

Towards a novel privacy-preserving access control

model based on blockchain technology in IoT. In

Proc. of EMENA-TSSL’16, pages 523–533. Springer.

Polge, J., Robert, J., and Le Traon, Y. (2021). Permissioned

blockchain frameworks in the industry: A compari-

son. ICT Express, 7(2):229–233.

Schiefer, M. (2015). Smart home deﬁnition and security

threats. In 9th Int. Conf. on IT Security Incident Man-

agement and IT Forensics, pages 114–118. IEEE.

Steichen, M., Fiz, B., Norvill, R., Shbair, W., and State,

R. (2018). Blockchain-based, decentralized access

control for IPFS. In Int. Conf. on Internet of Things

(Things’18), pages 1499–1506. IEEE.

Xu, X., Sun, G., Luo, L., Cao, H., Yu, H., and Vasi-

lakos, A. V. (2021). Latency performance model-

ing and analysis for hyperledger fabric blockchain

network. Information Processing & Management,

58(1):102436.

Xu, X., Weber, I., Staples, M., Zhu, L., Bosch, J., Bass, L.,

Pautasso, C., and Rimba, P. (2017). A taxonomy of

blockchain-based systems for architecture design. In

IEEE Int. Conf. on Software Architecture (ICSA’17),

pages 243–252. IEEE.

Yamashita, K., Nomura, Y., Zhou, E., Pi, B., and Jun,

S. (2019). Potential risks of Hyperledger Fabric

smart contracts. In Int. Workshop on Blockchain Ori-

ented Software Engineering (IWBOSE’19), pages 1–

10. IEEE.

Zhang, J. (2020). Deploying blockchain technology in the

supply chain. In Computer Security Threats, chap-

ter 5. IntechOpen.

Zhang, R., Xue, R., and Liu, L. (2019). Security and privacy

on blockchain. ACM Comput. Surv., 52(3).

Zou, Y., Meng, T., Zhang, P., Zhang, W., and Li, H. (2020).

Focus on blockchain: A comprehensive survey on

academic and application. IEEE Access, 8:187182–

187201.

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