big data

1. OPTIMIZING SHARE SIZE IN EFFICIENT AND ROBUST
SECRET SHARING SCHEME FOR BIG DATA
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GUIDED BY
Mr.K.SATHISH,M.Tech.,
AP/CSE
SUBMITTED BY
K.BOOMIKA
S.GOWTHAMI
M.HARINI
621819104001
621819104003
621819104004
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2. ABSTRACT
 This project describes optimized Secret sharing scheme which has been applied commonly in
distributed storage for Big Data.
 It is a method for protecting outsourced data against data leakage and for securing key management
systems
 The group data sharing scheme is implemented based on slepian–wolf data sharing management
algorithm
 We propose a new secret sharing scheme based on Slepian-Wolf coding embedded encoding algorithm
is implemented for secure data storage
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3. INTRODUCTION
 BIG Data is becoming a new era in data exploration
 The amount of data is increasing exponentially, and thus, a numerous applications are introduced such
as mobile computing , smart grid publish/subscribe services and most popular one is cloud or
distributed storage system
 To protect against the threats, secret sharing scheme is an ideal method which has been used more
popularly in distributed systems. Secret sharing scheme is used for distributing a secret among a group
of participants with the help of a data
 The secret can only be reconstructed when there are enough number of shares combining together. Each
share cannot be used alone to extract meaningful information
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4. OBJECTIVE
 To minimize the share size required for efficient and secure sharing of big data while maintaining data
confidentiality and security.
 To develop a robust secret sharing scheme that is resilient to potential attacks and disruptions during the
sharing process.
 To optimize the scheme to minimize computational overhead and processing time required for
generating and distributing shares.
 To ensure the scalability of the scheme to handle large volumes of data and support parallel processing
to improve efficiency.
 To evaluate the proposed method and compare it with existing methods to demonstrate its effectiveness
in terms of share size, efficiency, and robustness.
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5. LITERATURE SURVEY
S.NO TITLE AUTHOR &
YEAR
DESCRIPTION
1 A novel fully
homomorphic robust
secret sharing scheme
Longhui Ni
Fuyou Miao
2022
As an enhancement to traditional secret sharing,
robust secret sharing provides additional fault
tolerance. The fault tolerance means that the original
secret can be recovered even if some shares used in
reconstruction are incorrect. So far, there is a lot of
research work on robust secret sharing, however the
current schemes are all based on Shamir's scheme.
2 Robust Secret Image
Sharing Based on
Robust Chinese
Reminder Theorem
Xiaohui Jin
Fuyou Miao
2022
we propose a robust SIS (RSIS) scheme based on the
Robust Chinese Remainder Theorem (RCRT), namely
RSIS-RCRT, which can realize the reconstruction of the
secret image with high quality under a certain error in
the shadow image.
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6. S.NO TITLE AUTHOR& YEAR DESCRIPTION
3 Mining conditional functional
dependency rules on big data
Mingda Li
Hongzhi Wang
2020
We design the fault-tolerant rule discovery and
conflict-resolution algorithms to address the
low-quality issue of big data. We also propose
parameter selection strategy to ensure the
effectiveness of CFD discovery algorithms.
4 Big data analytics in support of
the under-rail maintenance
management at Vitória –
Minas Railway
Osvaldo Gogliano
Sobrinho
Liedi Ledi Mariani
Bernucci
2021
This paper describes an ongoing study using
data collected by an instrumented ore car on
Vitória–Minas Railway, operated by Vale in
Brazil. The research uses big data analysis
methods over collected data by the
instrumented car during its voyages.
5 Big Data Lakes: Models,
Frameworks, and Techniques
Alfredo Cuzzocrea
2021
big data lakes are prominent components of
emerging big data architectures. Basically, big
data lakes are the natural evolution of data
warehousing systems in the big data context,
and deal with several requirements deriving
from the well-known 3V nature of big data.
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7. EXISTING SYSTEM
 In existing system XOR network coding based encryption algorithm has been implemented
 Ramp secret sharing based algorithm has been implemented in existing system
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8. DISADVANTAGES
 High data leakage
 There is no secret protection
 Sharing of data repair possible
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9. PROPOSED SYSTEM
 The proposed system we present our proposed scheme SW-SSS leveraging Slepian-Wolf coding which
helps to reduce the share size and achieves exact-share repair property
 Homomorphic signature based scheme has been implemented for group data sharing algorithm
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 The share size and achieves exact-share repair property. The scheme is more efficient in terms of
storage, communication and computation costs.
 This is the general idea when applying Slepian-Wolf coding in our scheme. Note that our scheme
focuses on share generation, secret reconstruction and share repair algorithms. The share repair is
executed when a share is corrupted or has errors. How to check the corrupted share or detect errors
in a share are beyond the scope of this paper; however, several counter measures can be used to deal
with this problem such as homomorphic MAC
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10. ADVANTAGES
 High secure
 We can share high reliable data sharing algorithm
 Secrecy rate is high
 Share Size is data is high
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11. ARCHITECTURE DIAGRAM
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Data upload in cloud
One to many person
Data sharing
Secret sharing of
cloud protection
Secrecy analysis
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12. MODULES
 Cloud owner data uploading
 Secrecy data sharing
 Data sharing cloud protection
 Secrecy rate analysis
 Data analysis
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13. CLOUD OWNER DATA UPLOADING
 Instead of uploading the entire data in one go, the data can be split into smaller chunks. This can help to
reduce the share size and also make it easier to distribute the data among multiple servers.
 The threshold value is the minimum number of shares required to reconstruct the data. A higher
threshold value provides better security, but also increases the share size.
 An appropriate threshold value should be selected that balances the security requirements and share
size.
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14. SECRECY DATA SHARING
 Secret sharing is a cryptographic technique used to distribute a secret among multiple parties in such a
way that the secret can only be reconstructed when a predetermined number of parties collaborate.
 This technique is widely used in various applications such as secure data storage, access control, and
cryptographic key management.
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15. DATA SHARING CLOUD PROTECTION
 Data sharing in the cloud is becoming increasingly common, with many organizations using cloud
storage and sharing services to store and exchange large amounts of data.
 As data is shared across the cloud, it becomes vulnerable to security threats such as data breaches, data
loss, and unauthorized access. To address these security concerns, efficient and robust secret sharing
schemes can be used to protect data in the cloud.
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16. SECRECY RATE ANALYSIS
 Secrecy rate analysis is a method used to evaluate the performance of a secret sharing scheme by
quantifying the amount of secrecy that is maintained throughout the sharing process. In the context of
optimizing share size in an efficient and robust secret sharing scheme for big data, secrecy rate analysis can
be used to measure the effectiveness of the scheme in maintaining the secrecy of the shared data.
 The secrecy rate is defined as the ratio of the amount of secret information that can be reconstructed from
the shares to the total size of the shares. A high secrecy rate indicates that a large amount of secret
information can be reconstructed from a relatively small number of shares, which is desirable in a secret
sharing scheme.
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17. DATA ANALYSIS
 Big data refers to the large volumes of structured, semi-structured, and unstructured data that are
generated by businesses, organizations, and individuals every day.
 This data contains valuable insights that can help organizations make better decisions and gain a
competitive edge in their industriesThe big data also presents significant challenges when it comes
to data storage, analysis, and sharing.
 One of the challenges of sharing big data is how to maintain the confidentiality of the data while
allowing multiple parties to access and analyze it.
 This is where efficient and robust secret sharing schemes come into play. These schemes distribute a
secret among multiple parties in such a way that the secret can only be reconstructed when a
predetermined number of parties collaborate.
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18. PROBLEM STATEMENT
 The problem is to optimize the share size in efficient and robust secret sharing schemes for big data
while maintaining data security and confidentiality
 The scheme should be computationally efficient, as big data systems process massive amounts of data
 Therefore, the time required to generate and distribute shares must be minimized.
 The scheme should be robust to potential attacks such as collusion attacks, in which attackers combine
multiple shares to retrieve sensitive data. The scheme should also be resilient to any disruptions or errors
during the sharing process. The share size should be optimized to minimize the storage space and
network bandwidth required for sharing the data while maintaining data security. The scheme should be
scalable to handle large volumes of data and support parallel processing to improve efficiency.
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19. SYSTEM REQUIREMENTS
H/W SYSTEM CONFIGURATION:-
 processor - Pentium – IV
 RAM - 4 GB (min)
 Hard Disk - 200 GB
S/W SYSTEM CONFIGURATION:-
 Operating System : Windows 7 or 8
 Software : python Idle
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20. CONCLUSION
 In conclusion, optimizing share size in efficient and robust secret sharing schemes is crucial for
protecting sensitive information in big data systems. The use of secret sharing schemes can ensure data
confidentiality by dividing data into shares that can only be accessed when combined with other shares.
 However, the size of shares can pose limitations to the sharing process due to factors such as network
bandwidth, storage capacity, and computational overhead.To optimize share size, the scheme must be
efficient, robust, scalable, and maintain data security.
 The scheme should be computationally efficient to minimize the time required to generate and distribute
shares. It should also be robust to potential attacks and resilient to disruptions or errors during the
sharing process.
 The share size should be optimized to minimize the storage space and network bandwidth required for
sharing the data while maintaining data security. Finally, the scheme should be scalable to handle large
volumes of data and support parallel processing to improve efficiency
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21. FUTURE ENHANCEMENT
 New algorithms can be developed to optimize share size in secret sharing schemes. These algorithms
can leverage techniques such as compression, encryption, and error correction to minimize the size of
shares while maintaining data security.
 The performance of secret sharing schemes can be evaluated in real-world scenarios, such as in cloud
computing environments or with IoT devices. This will provide insights into how the schemes perform
in practical applications and identify areas for further improvement.
 The characteristics of the network, such as latency and bandwidth, can impact the performance of secret
sharing schemes. Therefore, future work can investigate the impact of network characteristics on the
efficiency and robustness of these schemes.
 The in optimizing share size in efficient and robust secret sharing schemes for big data will involve
developing new algorithms, evaluating performance in real-world scenarios, investigating the impact of
network characteristics, exploring hybrid approaches, and extending to new types of data.
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22. REFERENCES
 M. Cheraghchi, "Nearly optimal robust secret sharing", Des. Codes Cryptogr., vol. 87, no. 8, pp. 1777-
1796, Aug. 2019.
 P. Wang, X. He, Y. Zhang, W. Wen and M. Li, "A robust and secure image sharing scheme with personal
identity information embedded", Comput. Secur., vol. 85, pp. 107-121, Aug. 2019.
 Li Fulin, "A Threshold Multiple Secret Sharing Scheme Based on Hamming Code [J]", Journal of Hefei
University of Technology (Natural Science Edition), no. 5, pp. 711-714, 2021
 Liu Sijia, "Research and design of image sharing scheme based on QR code [D]", Strategic Support Force
Information Engineering University, 2019
 Gao Juntao, Yue Hao and Cao Jing, "Visual Multi-Secret Sharing Scheme Based on Random Grid
[J]", Journal of Electronics and Information, vol. 44, pp. 1-8, 2022.
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