The document proposes a system called SecNet that uses blockchain and AI to securely store, compute, and share data at scale on the internet. It aims to enable real big data and powerful AI by: 1) Using blockchain to allow trusted data sharing while guaranteeing data ownership. 2) Developing an AI-based secure computing platform to intelligently generate security rules and construct a trusted cyberspace. 3) Establishing a value-exchange mechanism where participants are rewarded for providing data or services, promoting more data sharing and better AI performance. The system is described as a way to address current issues where data is scattered and controlled by different stakeholders without trust, and complex data usage is difficult to

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“Securing Data With Blockchain and AI”
BACHELOR OF ENGINEERING IN COMPUTER SCIENCE AND
ENGINEERING
Submitted by
Amrutha S H (1HM20CS006)
Anusha D (1HM20CS008)
Kavyashree M (1HM20CS018)
Suma C R (1HM20CS037)
Under the guidance of
Wasimuddin,Asst professor,
Dept. of CSE
HMSIT,TUMAKURU
Presentation
on

2. Abstract
Data is the input for various artificial intelligence (AI) algorithms to mine valuable features, yet
data in Internet is scattered everywhere and controlled by different stakeholders who cannot
believe in each other, and usage of the data in complex cyberspace is difficult to authorize or to
validate. As a result, it is very difficult to enable data sharing in cyberspace for the real big data,
as well as a real powerful AI. In this paper, we propose the SecNet, an architecture that can enable
secure data storing, computing, and sharing in the large-scale Internet environment, aiming at a
more secure cyberspace with real big data and thus enhanced AI with plenty of data source, by
integrating three key components:
 blockchain-based data sharing with ownership guarantee, which enables trusted data sharing
in the large-scale environment to form real big data;
 AI-based secure computing platform to produce more intelligent security rules, which helps
to construct a more trusted cyberspace;
 trusted value-exchange mechanism for purchasing security service, providing a way for
participants to gain economic rewards when giving out their data or service, which promotes
the data sharing and thus achieves better performance of AI. Moreover, we discuss the typical
use scenario of SecNet as well as its potentially alternative way to deploy, as well as analyze
its effectiveness from the aspect of network security and economic revenue.
 INDEX TERMS: Data security, data systems, artificial intelligence, cyberspace.
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3. Introduction
 In cyber world everything is dependent on data and all Artificial Intelligence algorithms
discover knowledge from past data only, for example in online shopping application
users review data is very important for new comers to take decision on which product
to purchase or not to purchase, we can take many examples like health care to know
good hospitals or education institutions etc. Not all cyber data can be made publicly
available such as Patient Health Data which contains patient disease details and contact
information and if such data available publicly then there is no security for that patient
data.
 Now a days all service providers such as online social networks or cloud storage will
store some type of users data and they can sale that data to other organization for their
own benefits and user has no control on his data as that data is saved on third party
servers.
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System Requirements and Specification
Hardware Requirement
1. Processor Type: Pentium –IV.
2. ROM: 512 MB.
3. RAM: 4 GB.
4. Hard disk: 20 GB.
 Software Requirement
1. Operating System: Windows 2007/8/10/11
2. Script: python
3. Blockchain technology

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Literature Survey

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Proposed Methodology

7. Contd.. 7
 To overcome the issue we describe the concept called Private Data Centres (PDC) with
Blockchain and AI technique to provide security to user’s data. In this technique 3 functions will
work which describe below.
1) Blockchain: Blockchain-based data sharing with ownership guarantee, which enables trusted data
sharing in the large-scale environment to form real big data. In this technique users can define
access control which means which user has permission to access data and which user cannot
access data and Blockchain object will be generate on that access data and allow only those users
to access data which has permissions. In Blockchain object user will add/subscribe share data and
give permission.
2) Artificial Intelligence: AI-based secure computing platform to produce more intelligent security
rules, which helps to construct a more trusted cyberspace. AI work similar to human brain and
responsible to execute logic to check whether requesting user has permission to access shared
data. If access is available then AI allow Blockchain to display share data otherwise ignore
request.
3) Rewards: In this technique all users who is sharing the data will earn rewards point upon any
user access his data. trusted value-exchange mechanism for purchasing security service, providing
a way for participants to gain economic rewards when giving out their data or service, which
promotes the data sharing and thus achieves better performance of AI.

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 An increasing amount of personal data, including location information, web searching behavior,
user calls, user preference, is being silently collected by the built - in sensors inside the products
from those big companies, which brings in huge risk on privacy leakage of data owners.
 Moreover, the usage of those data is out of control of their owners, since currently there is not a
reliable way to record how the data is used and by who, and thus has little methods to trace or
punish the violators who abuse those data. That is, lack of ability to effectively manage data makes
it very difficult for an individual to control the potential risks associated with the collected data. In
cyber world everything is dependent on data and all artificial algorithms will gain knowledge from
previous data only.
 As the technology is increasing day by day, some of service providers such online social network
or cloud storage will store some type of users data and they can sale that data for their benefits and
user has no control on his/her data because the data is saved on third party servers.
 Drawbacks
1) There is no security for the user’s data. 2) User has no control on his data.
Existing System

9. Problem statement
 An increasing amount of personal data, including location information, web
searching behavior, user calls, user preference, is being silently collected by the built
- in sensors inside the products from those big companies, which brings in huge risk
on privacy leakage of data owners.
 Moreover, the usage of those data is out of control of their owners, since currently
there is not a reliable way to record how the data is used and by who, and thus has
little methods to trace or punish the violators who abuse those data.
 Lack of ability to effectively manage data makes it very difficult for an individual to
control the potential risks associated with the collected data.
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[1] Health Insurance. (2021). Health Insurance in India. [Online]. Available:
https://en.wikipedia.org/wiki/Health_insurance_in_India
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Data, Jun. 2014, pp. 762–765.
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risks,’’ Geneva Papers Risk InsuranceIssues Pract., vol. 43, no. 3, pp. 359–396, Jul. 2018.
[6] R. Dutt, ‘‘The impact of artificial intelligence on healthcare insurances,’’ in Artificial Intelligence in Healthcare.
Amsterdam, Netherlands: Elsevier, 2020, pp. 271–293.
[7] P. Kumar and H.-J. Lee, ‘‘Security issues in healthcare applications using wireless medical sensor networks: A
survey,’’ Sensors, vol. 12, no. 1, pp. 55–91, 2012.
[8] M. Li, W. Lou, and K. Ren, ‘‘Data security and privacy in wireless body area networks,’’ IEEE Wireless Commun.,
vol. 17, no. 1, pp. 51–58, Feb. 2010.
[9] J. Heurix, M. Karlinger, and T. Neubauer, ‘‘Pseudonymization with metadata encryption for privacy-preserving
searchable documents,’’ in Proc. 45th Hawaii Int. Conf. Syst. Sci., Jan. 2012, pp. 3011–3020.
[10] K. M. Kumar, S. Tejasree, and S. Swarnalatha, ‘‘Effective implementation of data segregation & extraction using
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2016, pp. 1–5.
References

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