# From Blockchain to Hashgraph: Distributed Ledger Technologies in the Wild

Zuhaib Akhtar  Department of Computer Engineering
Aligarh Muslim University
Aligarh, India
<EMAIL_ADDRESS>
###### Abstract

With the introduction of the term blockchain in 2008, it’s interest has been
increasing in the community since the idea was coined. The reason for this
interest is because it provides anonymity, security and integrity without any
central third party organisation in control of data and transaction. It has
attracted huge interest in research areas due to its advances in various
platforms, limitations and challenges. There are various Distributed Ledger
Technologies that demonstrates their special features which overcome
limitations of other platforms. However, implementations of various
distributed ledger technologies differ substantially based on their data
structures, consensus protocol and fault tolerant among others. Due to these
variations, they have a quite different cost, performance, latency and
security. In this paper, working and in-depth comparison of major distributed
ledger technologies including their special features, strengths and weaknesses
is presented and discussed by identifying various criteria.

###### Index Terms:

Distributed ledger technology, Blockchain, Sidechain, Tangle, Hashgraph

††publicationid: pubid: 978-1-7281-3455-0/19/$31.00 ©2019 IEEE

##  I INTRODUCTION

The advent of protocols like TCP/IP and evolution of the internet led to the
development of centralised systems. These systems are used in various domains.
One such domain is centralised transactions. Currency transactions between
companies or persons are generally centralised and these centralised servers
are controlled by third party organisations. As they are providing services, a
transaction fee is charged by them. All the information and data are managed
and controlled by them, rather than the two entities that were involved in the
transaction. With the development of blockchain technology, this issue has
been resolved. Its goal is to create decentralised network, where there is no
control of the third party over data and transactions. It is a distributed
ledger technology (DLT) which stores data and transaction in the network
itself rather than in any centralised system. It guarantees safety, privacy,
integrity and transparency to the user [1]. People can transfer the
cryptocurrency to anyone and even to those who do not have a bank account.
However, blockchain has faced criticism for its scalability and cost.
Therefore, new Directed Acyclic Graph (DAG) based DLTs have been introduced
such as hashgraph. It promises to remove most of the shortcomings of
blockchain based DLTs.
In this study, working of major DLTs and systematic comparison between various
DLTs are presented. The objective behind this effort is to define and compare
the main properties and crucial advantage of different technologies, and
identify the current state of DLTs. Based on this research, a set of quality
criteria has been identified which helps to compare their special features and
shows the sequential progress of DLTs over the years.

##  II Major distributed ledger technologies review

Major goal of DLT is to bring trust into the system in which there is no
central authority which can be trusted. In such a system, there is no mutual
trust between users. At its core, DLT are data structures including functions
to handle them. Each DLT has its own data model and mechanism, but all are
based on three technologies i.e., distributed peer-to-peer network, public key
cryptography and consensus algorithm. As in distributed environment, no one
can be trusted, public key cryptography brings security and digital identity
for every system in the network. Each participant has a public and a private
key pair which helps in recording transactions in Distributed Ledger (DL).
Members enforce control on objects managed by DL with the help of digital
identity. A distributed peer-to-peer is meant to avoid single point of
failure, scale easily and effectively in the network, and to avoid hostile
takeover by a member or a group in the network. Consensus protocol is the
heart of any DL. It brings trust into the system where one member cannot trust
the other i.e., all members agree on the one and only one version of truth. A
consensus algorithm must be deterministic, fair, secure and fast. It must
tolerate byzantine behavior [2]. In byzantine consensus, correct processes or
systems agree on a value and eventually entire system agrees on it even if
some systems are malicious. Following sub-sections discusses major DLTs along
with their features, strengths and weaknesses.

###  II-A Blockchain

Blockchain is immutable, distributed and decentralised ledger which stores
transactions in the form of blocks. The blocks are appended as time passes and
is accessible to all members of the network. Blocks are connected to each
other in the form of linked list. The major advantage is that it makes
difficult for any member to tamper with the contents of the block as any
change in it will break the chain of blocks. This is because every block
contains hash of previous block. The elements of blockchain are described as
follows:

  * •

Transaction is an information which is digitally signed by a member in the
network. Group of transactions are known as blocks and they are appended to
blockchain upon validation.

  * •

A blockchain is a ledger which stores all the blocks that are created in the
network. It utilises hash of previous block which connects all the blocks
together, which is comparable to linked list.

  * •

A consensus algorithm decides which block will be added to the blockchain.

Bitcoin and ethereum are two most common applications of blockchain. The
consensus mechanism used in bitcoin is proof-of-work (POW). The core idea of
this algorithm is to give rights (to add a block in the blockchain) to a
member in the network that solves tough mathematical problem first. This tough
mathematical problem is inverse hash calculation which is done by specialised
hardware. Whosoever solves first gets the right to add next block to the
blockchain. This is known as mining. However, these specialised hardware
consumes a lot of power and are costly. Ethereum solves this issue by using
proof-of-stake consensus algorithm. It replaced complex inverse calculation
with an alternative approach which involves stake (cryptocurrency) of user.
Longer a user holds stake, more rights it will get. Hence, it saves problem of
wastage of resources. Some features of blockchain are given as follows:

  * •

Implementations: There is large number of implementation of blockchain based
technologies and it is increasing. These technologies target different domain.
However, main goal of blockchain is to create a DL that allows all members in
the network to agree on and share the same version of truth.

  * •

Smart contracts: Executable program that resides in blockchain and when some
specific conditions are met, they get executed. They are not executed until
their invoking transaction gets into the block.

  * •

Miners: Mining provides incentives to that member who mines the new block
i.e., one who gets the right to add a new block to blockchain.

###  II-B Sidechain

Sidechain combines two different blockchain architectures to remove existing
shortcomings of blockchain in terms of performance, privacy and security [3].
‘Main’ blockchain processes all the global requests of members and ‘sidechain’
can be used to manage local requests. Even if sidechain gets compromised,
blockchain can function on its own.
As number of nodes (members) grow in blockchain, it becomes difficult to
achieve consensus in the network. Sidechain can be seen as dividing the main
blockchain into segments. Sidechains are sub-networks and any request
submitted to it is processed locally. Hence, rather than having single large
main blockchain, it groups blockchain into number of sub-blockchains and a
main blockchain. Each sidechain in the network can be used by a company or
members with common interests. Digital assets can be moved back and forth
between sidechains at a fixed exchange rate by the two-way peg mechanism.
Sidechain can also be used to hide data from others. Sidechain is connected to
the main blockchain through special nodes which are called validators whose
work is to validate transactions that are occurring locally. It makes
sidechain independent from the main blockchain.
Sidechain extends all the properties of blockchain but solves its major
issues. Important features of sidechain are given as follows:

  * •

Scalability: It resolves scalability issue of blockchain by creating sub-
networks.

  * •

Privacy: It also solves privacy issues by imposing constraint over who can
access which part of data in the network.

###  II-C Tangle

Tangle if one of the platforms that is very well suited for Internet of Things
(IOT). It was developed by IOTA. Tangle uses DAG based data structure. Each
vertices in the DAG is called site and edges between sites corresponds to
transaction approval.
IOTA was developed to solve many problems related to blockchain. It does not
have miners, concept of blocks or transaction fees. Removing transaction fees
is important in IOT, where M2M micro-transaction is expected. This is because
transaction fee can be greater than the transaction itself. As there are no
miners, any member that issues a new transaction, as a new site x, then it
must select two existing sites y and z and validates those transactions by
doing small POW. POW work algorithm in IOTA is called hashcard which works
well in IOT devices that generally have low computation power. In IOTA, there
is no upper bound on rate of transaction by the network.
Initial vertices are called genesis that holds all the crypto-currency of the
system. Sites that are not yet validated are called tips. Node (a member) can
choose any site for validation but honest nodes generally follows tip
selection procedure. It starts from genesis and does weighted random walk till
tip is reached. Weight of the site is dependent upon the number of validation
it received previously.
Currently, IOTA validates transaction using coordinator node. More precisely,
it issues a transaction every few minutes which is known as milestone. All the
transactions referenced by milestone are validated. It is a temporary solution
and sooner or later it will use a distributed solution known as Markov-chain
Monte-Carlo algorithm.
Tangle proposes an Markov-chain Monte-Carlo algorithm which probabilistically
checks validation of transaction. It runs tip selection procedure x times. It
checks, for a given transaction, how many of the selected tip references it.
Let’s say these tips are y in number. Then, transaction is validated with the
confidence y/x. Some features of tangle are as follows:

  * •

Scalability: As all members in the network verify transactions, increase in
the number of members results in faster validation. These members can also
validate transactions in parallel.

  * •

Micro-transactions or Transaction fees: IOTA combined miner and validator into
one role i.e., if one wants to issue a transaction, then it should support
network by validating two transactions using small POW. Therefore, instead of
paying miner additional fee for validating a transaction, member uses its own
computing power to validate it.

  * •

Decentralisation: As there are no special validators of transactions like
miners in the network, therefore, there isn’t any problem of concentration of
resources or computing power unlike bitcoin.

  * •

Quantum resistant: To generate public address, IOTA requires use of Winternitz
One Time Signature scheme which is supposed to be quantum resistant. It is
advised in IOTA’s documentation that one should use generated address once as
parts of private key might get disclosed.

###  II-D Hashgraph

Hashgraph was developed by the co-founder and chief technology officer of
Swirlds, Leemon Baird in 2016. It is a platform which can work in a malicious
environment and provides distributed consensus. Swirlds is a permissioned
network i.e., only authorised members can join the network and every member
know about all the members in the network.
At an abstract level, hashgraph can be considered a structure which has
columns and each member is represented by a column in the network. All the
columns have many vertices. Each of the vertex is called an event. A user in
the network basically performs two actions; (i) At any time user can create an
event and submit it, (ii) User randomly picks a member in the network and
gossips all the information that it know i.e., it sends the information to the
member about the creation of event. The distribution of events takes place
with the help of gossip-about-gossip protocol. These events store four
different types of information in its data structure:

  * •

Zero or more transactions submitted by the user

  * •

Timestamp or time at which event was created

  * •

Hash of the previous event created by the user

  * •

Hash of the another user’s event sending the gossip

User then places his digital signature and gossips about this event. The
information, in addition to transactions in an event, serves various purposes.
Incorporation of two different hashes allow members to know the history of
transactions i.e., from where transaction was originated and where it was
directed (to whom it was gossiped). Hashgraph uses this information to build
DAG of events. DAG is updated as members gossips in the network. Signing an
event helps in identifying the creator of event and no-one can tamper with
that event. It is pretty clear why protocol for distributing of event is known
as gossip-about-gossip. Member sends not only what he know (gossip) but when
other members learned it (gossip-about-gossip). This protocol has logarithmic
running time. Suppose, at time t=0 a member creates an event, at time t=1 that
member sends that event to randomly chosen other member. Hence, two members
know that event at time t=1. Similarly, those two members shares that event
with two other members in the network at time t=2. Hence, four members know
about that event at time t=2. As we can see, at time t=3, eight members will
know about that event. Hence if there are ‘n’ members in the network, it will
take t=Log(n) for that event to spread in the entire network. Hence, gossip
spreads in the network at an exponential rate.
Subsequent subsection discusses hashgraph consensus algorithm and its
assumptions.

####  II-D1 Terminologies used in Hashgraph Consensus Algorithm

  * •

Supermajority is a number which is greater than 2/3rd of any positive integer.

  * •

An event A is parent/ancestor of event B if there is an edge from A to B.

  * •

An event A is self-parent/self-ancestor of event B if A and B are created by
same node.

  * •

An event A can see event B if B is an ancestor of A and creator of B does not
have two ancestors of B such that these two events are not ancestors of each
other.

  * •

An event A can strongly see B, if B is seen by more than supermajority of
distinct nodes in the network.

  * •

First event of every member in a round (will be discussed in subsequent
section) is termed as witness.

####  II-D2 Assumptions of Hashgraph Consensus Algorithm

  * •

number of members or nodes in the network is fixed and identity of all nodes
in the network is known.

  * •

Number of malicious nodes in the network is less than 1/3rd of the total
members in the network.

  * •

Signature of members cannot be forged and hash function used is resistant to
collision.

  * •

Messages transfer between members of the network is asynchronous. This means
that adversary can delay the message but it will eventually reach the whole
network.

####  II-D3 Hashgraph Consensus Algorithm

1Run two loops in parallel;

2 while  _True_ do

3 Sync all known events to a random member;

4

5while  _True_ do

6 receive a sync;

7 create a new event;

8 call divideRounds;

9 call decideFame;

10 call findOrder;

11

Algorithm 1 Hashgraph Consensus Algorithm

Hashgraph consensus algorithm run two loops in parallel. First loop gossips
with other members in the network, sending entire history of transactions.
Second loop has three sub-protocols. Node in the network wait for the gossip
from any other node. After it receives gossip (sync), it takes hash of that
gossip, hash of its previous event, some new transaction and places timestamp
of that event. Then event is signed by its creator. After that, node runs
three sub-procedures to achieve consensus on the order of events. These are
given below:

  * •

DecideRound: First event of every node is assigned round number 1. After that
if any event can strongly see supermajority of round r witnesses, then it is
assigned round number r+1. Otherwise, it will be r.

  * •

DecideFame: A witness in round r can be decided famous. If witness in round
r+1 can see witness in round r, it votes YES. Otherwise, it votes NO. Each of
the witness in round r+1 votes for witness in round r in similar fashion.
These votes are counted by witness in round r+2. If total count of YES votes
exceeds supermajority, then it is decided that witness in round r is famous
and election is over. If supermajority of votes are NO, then witness in round
r is not famous. If witness in round r+2 cannot decide election, then other
witness count votes to decide election. Witness in round r+2 count YES/NO vote
of witness in round r+1, if it strongly sees witness in round r+1. It might be
possible that none of the witness in round r+2 can decide election. In such
case, witness in succeeding round will decide election. There might be a case
where election might not end. To counter this problem, coin round is
conducted. Witness merely vote when collecting supermajority in coin round
(not decide). Hence, election will end with probability 1.

  * •

FindOrder: It decides round received of each event. Round received of event in
round r is same as round number of famous witness of round r, if it can be
seen by every famous witness of round r. Now, to determine consensus timestamp
of event (say X in round r), then immediate ancestor of round r witness and
descendant of X are found. Let us suppose that these events are E1, E2, E3,
and so on upto En. Timestamp of all these events are taken which was assigned
by their creator. Then median of these timestamp is assigned as a consensus
timestamp of X.

Some important features of hashgraph are listed below:

  * •

Fairness: Consensus timestamp is assigned to all events which ensures that
their order is correct.

  * •

Fast: It has high throughput (number of transactions/second) as it works on
gossip-about-gossip protocol. It ensures that propagation of events is fast in
the network as it randomly selects a member and sends the message without any
condition or boundation. It does not need to wait to get the votes of member
to assign consensus timestamp.

  * •

Virtual voting: Every member maintains full history of transactions by
maintaining DAG of events. No member is required to send it’s vote to other
member. This is because once hashgraph DAG is build, it is easy to know how a
node will vote, since every node has all the information of what each node
know and when they knew it. This information is used for finding order of
transactions. Building a DAG on every node helps to achieve consensus on order
of events independently. This saves a huge amount of bandwidth as node does
not have to transfer their vote to other nodes. This is because other node has
required amount of information of how a node will vote in election.

  * •

Highly efficient: No event is ever discarded in hashgraph. All the events are
used when DAG is formed. Bandwidth requirement is also very less as only
information related to transactions are transmitted and no votes are
transmitted over the network.

##  III RELATED WORK

Sankar et al. compared blockchain technologies on the basis of their consensus
protocols [9]. Xu et al. compared blockchain on their advantages and
disadvantages [10]. Lin et al. reviewed blockchain with the focus of security
[11]. Application of different blockchain based DLT in the field of IOT was
reported in [12]. Comparison of nano, a DAG based DLT, was done with
blockchain on the surface by Beni et al. [13]. Schueffel et al. reviewed some
DAG based DLT and blockchain in the literature.
Studies comparing blockchain and various DAG based DLT on extensive criteria
and providing a big picture on current state of DLT could not be traced. This
literature does that which helps to predict the direction in which DLT based
technology is moving forth.

##  IV Criteria for Evaluation of Distributed Ledger Technologies

By going over white papers of various DLTs discussed in previous
sections[4-7], it was found that each of the DLTs tried to solve the
shortcomings of present technologies by targeting set of features to
distinguish itself from others. Although, targeting only set of features bring
some disadvantages in these DLTs which are discussed in subsequent sections.
Nowadays, new technologies brings unprecedented novelty and hence, it
generates immense confusion. To bring innovation, one should have thorough
knowledge of existing technologies, use case, strengths and weaknesses. Set of
criteria mentioned in this literature will definitely help anyone to get a
better view of current standings of various DLTs discussed in this paper.
These criteria are listed below:

  1. 1.

Architecture: how information is stored and arranged in the network.

  2. 2.

Transaction: representation of transaction in the network.

  3. 3.

Consensus: how transactions are validated in the network.

  4. 4.

Copyright: whether rights given to use that technology

  5. 5.

Latency: time taken by the network to validate a transaction.

  6. 6.

Byzantine Fault Tolerant (BFT): Systems that tolerate failures that belong to
Byzantine Generals’ Problem [2].

  7. 7.

Privacy: whether DLT ensures privacy

  8. 8.

Fee: Cost to submit a transaction

  9. 9.

Throughput (tps): rate of transactions administered by the network

  10. 10.

Security: extent to which network grantees security

  11. 11.

Fairness: Transaction A will appear before transaction B in the consensus
order, if A was created before B.

  12. 12.

Cost: cost of participation

  13. 13.

Maturity: present state of technology

  14. 14.

Setting: whether technology is public or private

  15. 15.

Competition: whether nodes are competing in the network to create block

  16. 16.

Scalable: affect on throughput when size of network is increased

  17. 17.

Platform: environment where technology is running

##  V Comparative Evaluation of Distributed Ledger Technologies

Quality criteria found in the previous section is used to evaluate DLTs on
various grounds. The comparative results of various DLTs are shown in Table 1.
Blockchain has linked list data structure. Every element is known as a block
and each block stores group (around 3500) of transactions. Sidechain extends
this very idea and implements various linked list associated to each other.
Tangle and hashgraph have DAG based data structure where each entity in the
DAG represents a transaction in the former and each element in DAG is an event
which store group of transactions in the latter.
If the main chain in blockchain branches off, then longest chain is taken as a
main chain and others are pruned off. This is because it assumes that true
nodes have more than 50% of the computing power and hence, they will generate
the longest chain even if malicious node try to manipulate the chain. DAG
based chain continuously branches and converges later i.e., no block is ever
stale and every block is used unlike blockchain.
Most of the DLTs are not fair unlike hashgraph. Fairness is a property where
if a transaction A is generated earlier than transaction B, then A should
appear before B in the consensus order of transactions. Only hashgraph is fair
among various DLTs. Blockchain based DLTs are not fair as it is upto a miner
who decides which transaction will go in which block.
Byzantine fault tolerant is a system which will eventually achieve consensus
in the network even if some members are malicious and are trying to manipulate
results or halt consensus. Blockchain is not Byzantine fault tolerant because
nobody knows when complete consensus is achieved, rather probability of
confidence increases as time passes. Whereas, Hashgraph is proven to be fully
asynchronous Byzantine Fault Tolerance (aBFT) which is gold standard of
security in the field of distributed systems [8].
Now, if we compare DLTs on the basis of participants, we find that blockchain
has issuers who submits a transaction for validation and miners who maintains
the integrity of the blockchain. There is coordinator node in sidechain which
is present in each of the blockchain. IOTA has a coordinator which validates
transaction and others are general users who submits the transaction for
validation known as entities. Users of hashgraph are known to Swirlds and
every member is known to every other member. Hence, all are allowed to
validate transactions.
To maintain integrity of the network, consensus is required on state of the
network. Blockchain uses POW and so does sidechain but with lower difficulty
as it is permissioned network. Tangle uses coordinator node which issues
transactions and, whichever transaction is referenced by it are considered
confirmed. Hashgraph uses virtual voting to achieve consensus among members.

TABLE I: Comparision of DLTs on various criteria Criteria |  Blockchain |  Sidechain |  Tangle |  Hashgraph
---|---|---|---|---
Architecture |  Linked List Blocks |  Multiple linked list |  DAG |  DAG
Transaction |  grouped in block |  Grouped in block |  separate entity |  Grouped in Event
Consensus |  POW (SHA-256) |  POW (Ethash) |  POW (hashcard) |  Virtual voting
Copyright |  open source |  open source |  open source |  patented
Latency |  a few minutes |  a few minutes |  a few seconds |  less than 10 seconds
BFT |  No |  No |  No |  Yes
Privacy |  low |  high |  low |  low
Fee |  yes |  yes |  no |  no
Throughput |  5-20 tps |  limited by main chain |  500-800 tps |  100,000 tps
Security |  high |  high |  high |  high
Fairness |  No |  No |  Not yet |  Yes
Cost |  high |  high |  low |  low
Maturity |  Many implementations |  Experimental |  Experimental |  Experimental
Setting |  public |  private and public |  private |  private
Competition |  yes |  yes |  no |  no
Scalable |  limited |  yes |  yes |  yes
Platform |  Ethereum, Bitcoin, etc |  Monax |  IOTA |  Hedra

##  VI RECENT DEVELOPMENTS

Facebook introduced libra cryptocurreny which will be launched in first half
of 2020. Initially some big players will be allowed to join the network for
validation such as visa and PayPal, although, Facebook has promised that they
will migrate to permissionless network within 5 years.
It will be governed by Libra association and is built on secure scalable and
reliable Libra blockchain which is open source. Rather than having blocks for
storing transactions, it records the history of transactions in single data
structure. Throughput of Libra will be around 1000 tps which is not as fast as
hashgraph. But, throughput is reasonable as visa does around 1700 tps and for
PayPal, it is around 193 tps. Consensus mechanism in Libra is known as
LibraBFT. This protocol will work in environment where 1/3rd nodes can be
Byzantine in the network. Libra is Byzantine fault tolerant, whereas hashgraph
is asynchronous byzantine fault tolerant (aBFT, which is highest level of
security) and the only DLT that is mathematically proven to be aBFT.

##  VII Discussions

Some of the issues with blockchain is its performance not meeting current
needs, forks in the chain and transaction fee which might be greater than
transaction itself. Sidechain tried to solve problem of scaling related to
blockchain but it also has some limitations as it doesn’t remove miners from
the picture which require incentives. Hashgraph and tangle solved many
problems of blockchain. For example, they both have better throughput than
blockchain based DLT. Tangle removed the transaction fee from the network as
user who submits the transaction for validation has to validate other
transaction. Hashgraph uses gossip-about-gossip and virtual voting to increase
throughput and efficiency of the network. It might be tempting to say tangle
and hashgraph are better than blockchain based DLT, but there is much more to
it. Two major blockchain platforms (Bitcoin and Ethereum) have been studied,
but there are other blockchain platforms that have better performance. For
example, Hyperledger (which runs in private setting) has throughput (around
700 tps) same as that of tangle. The reason why blockchain based DLT like
bitcoin and ethereum are slow because they are public. In Public setting,
anybody can join the network and start mining and can leave the network at
their own will. Public blockchain completely removes the middleman (eg bank)
from the picture. In public setting, no one can be trusted and hence, single
party is now allowed to control the network. There is a choice between speed
and decentralisation. Public blockchain chooses decentralisation over speed as
it is the entire point of having public blockchain. Hence, throughput of the
network is suppressed intentionally. This is done to reduce the number of
branches or forks in the chain and allow block to completely propagate
throughout the network, so that blockchain remains sync and fraudulent
transactions are prevented. The reason why Hyperledger, tangle and hashgraph
are fast because it is private and permissioned. Not anyone can join the
private network. Only trusted parties are allowed to join and verify
transactions. Therefore, it is kind of a ‘centralised system’. Private
blockchain choses speed over complete decentralisation. This allows much
greater throughput and efficiency while transactions are validated. Hashgraph
takes private blockchain to next level by introducing consensus mechanism that
increases throughput many times greater than any other DLT.
With the introduction of hashgraph followed by Libra, private DLT are picking
up pace, it is likely that gap between public and private DLT will widen.
Public blockchain is totally decentralised (and hence, no trust) and problem
is to make consensus on transactions fast such that they are secure in the
network where there is no mutual trust. Whereas, private blockchain is
partially distributed (partial trust) and throughput is high but it is not
open to public.
As private DLT is controlled by organisation, people might not have complete
trust in the system as these corporations might act according to their
interests. Bad actors can gain control if allowed to enter into the private
network. Hashgraph has shown some progress as its network will achieve
consensus if malicious nodes is less than 1/3rd of the total nodes in the
network.
The main idea of blockchain was to remove trusted party completely from the
picture is not completely met in private DLT. A perfect DLT should be best of
both the worlds i.e., completely decentralised and high throughput.
Although, blockhain based DLT such as bitcoin is public rather than
permissioned, but a recent study shows 74% of the bitcoin miners are from
China. That is, 74% of the computing power resides in China which means that
it is susceptible to 51% attack (POW assumes that more than 50% of the
computing power is controlled by trusted nodes).

##  VIII CONCLUSION

Huge potential is shown by Distributed Ledger Technologies that could be used
in various industries. They are posing threat to the existence of present
centralised third party organisations like banks which charge for their
services. In this paper, major DLTs were comprehensively reviewed and were
compared on the basis of criteria mentioned in preceding section. Public
blockchain such as bitcoin and ethereal were among the first DLTs that brought
decentralisation, trust, security and low operational cost. However, they
lacked the throughput required to meet needs of current services like PayPal
among other shortcomings. DAG based solutions have claimed to solve many of
challenges by public blockchain. IOTA is faster, but it still uses centralised
coordinator for validation. Hashgraph on the other hand is much faster, fairer
and secure than public blockchain. Both IOTA and hashgraph works in
permissioned environment which is inconsistent with the idealogy of removing
trusted third party.

## References

  * [1] Jingjing Gu, Binglin Sun, Xiaojiang Du, Jun Wang, Yi Zhuang, Ziwang Wang, ”Consortium Blockchain-Based Malware Detection in Mobile Devices”, Access IEEE, vol. 6, pp. 12118-12128, 2018.
  * [2] I. Abraham, G. Gueta, D. Malkhi, L. Alvisi, R. Kotla, J. P. Martin, Revisiting Fast Practical Byzantine Fault Tolerance , 2017, [Available] Online: https://arxiv.org/abs/1712.01367.
  * [3] Ali, M.S., Dolui, K., Antonelli, F.: IoT data privacy via blockchains and IPFS. In: Proceedings of the Seventh International Conference on the Internet of Things, p. 14. ACM (2017)
  * [4] Satoshi Nakamoto, Bitcoin:A Peer-to-Peer Electronic Cash System, www.cryptovest.co.uk, October 31, 2008
  * [5] Luke Dashjr Mark Friedenbach Gregory Maxwell Andrew Miller Andrew Poelstra Jorge Timn Adam Back, Matt Corallo and Pieter Wuille. Enabling blockchain innovations with pegged sidechains. 2012.
  * [6] Serguei Popov. The Tangle, https://iota.org/IOTA_Whitepaper.pdf 2016, [Online; accessed on 25th June-2019].
  * [7] L. Baird, The Swirlds Hashgraph Consensus Algorithm: Fair, Fast, Byzantine Fault Tolerant, Swirlds Tech Report Swirlds-TR-2016-01, 2016.
  * [8] Miguel Castro and Barbara Liskov, Practical Byzantine Fault Tolerance, Proceedings of the Third Symposium on Operating Systems Design and Implementation, New Orleans, USA, February 1999, Pp. 173186
  * [9] L. S. Sankar, M. Sindhu and M. Sethumadhavan, ”Survey of consensus protocols on blockchain applications,” 2017 4th International Conference on Advanced Computing and Communication Systems (ICACCS), Coimbatore, 2017, pp. 1-5. doi:10.1109/ICACCS.2017.8014672
  * [10] Xu, X., et al.: A taxonomy of blockchain-based systems for architecture design. In: 2017 IEEE International Conference on Software Architecture (ICSA), pp. 243– 252. IEEE (2017)
  * [11] Lin, I.C., Liao, T.C.: A survey of blockchain security issues and challenges. IJ Netw. Secur. 19(5), 653–659 (2017)
  * [12] Pahl, C., El Ioini, N., Helmer, S.: A decision framework for blockchain platforms for IoT and edge computing. In: International Conference on Internet of Things, Big Data and Security (2018)
  * [13] F. M. Benčić and I. Podnar Žarko, ”Distributed Ledger Technology: Blockchain Compared to Directed Acyclic Graph,” 2018 IEEE 38th International Conference on Distributed Computing Systems (ICDCS), Vienna, 2018, pp. 1569-1570. doi: 10.1109/ICDCS.2018.00171
  * [14] Schueffel, Patrick, Alternative Distributed Ledger Technologies Blockchain vs. Tangle vs. Hashgraph - A High-Level Overview and Comparison (December 15, 2017). Available at SSRN: https://ssrn.com/abstract=3144241 or http://dx.doi.org/10.2139/ssrn.3144241