Big Data Privacy using Fully Homomorphic Non-deterministic Encryption

被引:0
|
作者
Patil, Tejashree B. [1 ]
Patnaik, Girish Kumar [1 ]
Bhole, Ashish T. [1 ]
机构
[1] North Maharashtra Univ, SSBTs Coll Engn & Technol, Dept Comp Engn, Jalgaon, Maharashtra, India
关键词
Security and Privacy; Big Data; Encryption; Non-deterministic; Fully homomorphic encryption;
D O I
10.1109/IACC.2017.33
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
Big data is a large amount of digital information. Now days, data security is a challenging issue that touches several areas along with computers and communication. The security of data which stored online has become a main concern. Several attackers play with confidentiality of the user. Cryptography is a approach that provide data security to the user. Despite of huge efforts to protect sensitive data, hackers typically manage to steal it. Computing with encrypted data is strategies for safeguarding confidential data. The partial homomorphic encryption is specialized for only one operation on the encrypted data. For example the Pailliers encryption scheme performs only one mathematical operation on encrypted numerical data and is successful to compute the sum of encrypted values. The Pailliers encryption scheme is unable to do multiple mathematical operations on encrypted numerical data. The proposed encryption algorithm computes more than one mathematical operation on encrypted numerical data thereby further protecting the encrypted sensitive information.
引用
收藏
页码:138 / 143
页数:6
相关论文
共 50 条
  • [1] A Faster Fully Homomorphic Encryption Scheme in Big Data
    Wang, Dan
    Guo, Bing
    Shen, Yan
    Cheng, Shun-Jun
    Lin, Yong-Hong
    2017 IEEE 2ND INTERNATIONAL CONFERENCE ON BIG DATA ANALYSIS (ICBDA), 2017, : 350 - 354
  • [2] Privacy-Preserving Auction for Big Data Trading Using Homomorphic Encryption
    Gao, Weichao
    Yu, Wei
    Liang, Fan
    Hatcher, William Grant
    Lu, Chao
    IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING, 2020, 7 (02): : 776 - 791
  • [3] CryptoBlaze: A Partially Homomorphic Processor with Multiple Instructions and Non-Deterministic Encryption Support
    Irena, Florencia
    Murphy, Daniel
    Parameswaran, Sri
    2018 23RD ASIA AND SOUTH PACIFIC DESIGN AUTOMATION CONFERENCE (ASP-DAC), 2018, : 702 - 708
  • [4] On the security of fully homomorphic encryption for data privacy in Internet of Things
    Peng, Zhiniang
    Zhou, Wei
    Zhu, Xiaogang
    Wu, Youke
    Wen, Sheng
    CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE, 2023, 35 (19):
  • [5] Privacy Preserving Data Retrieval on Data Clouds with Fully Homomorphic Encryption
    Bulbul, Busranur
    Altilar, D. Turgay
    2019 4TH INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND ENGINEERING (UBMK), 2019, : 344 - 349
  • [6] Effectiveness of Fully Homomorphic Encryption to Preserve the Privacy of Biometric Data
    Torres, Wilson Abel Alberto
    Bhattacharjee, Nandita
    Srinivasan, Bala
    16TH INTERNATIONAL CONFERENCE ON INFORMATION INTEGRATION AND WEB-BASED APPLICATIONS & SERVICES (IIWAS 2014), 2014, : 152 - 158
  • [7] Enhancing Privacy in Face Analytics Using Fully Homomorphic Encryption
    Yalavarthi, Bharat
    Kaushik, Arjun Ramesh
    Ross, Arun
    Boddeti, Vishnu
    Ratha, Nalini
    2024 IEEE 18TH INTERNATIONAL CONFERENCE ON AUTOMATIC FACE AND GESTURE RECOGNITION, FG 2024, 2024,
  • [8] Big Data analytics for privacy through ND-homomorphic encryption
    Saravanan, S.
    Poornima, N.
    JOURNAL OF CONTROL AND DECISION, 2023, 10 (01) : 64 - 71
  • [9] Ensuring confidentiality and privacy of cloud data using a non-deterministic cryptographic scheme
    Dawson, John Kwao
    Twum, Frimpong
    Acquah, James Benjamin Hayfron
    Missah, Yaw Marfo
    PLOS ONE, 2023, 18 (02):
  • [10] Privacy-Preserving Data Synchronization Using Tensor-based Fully Homomorphic Encryption
    Gai, Keke
    Wu, Yulu
    Zhu, Liehuang
    Qiu, Meikang
    2018 17TH IEEE INTERNATIONAL CONFERENCE ON TRUST, SECURITY AND PRIVACY IN COMPUTING AND COMMUNICATIONS (IEEE TRUSTCOM) / 12TH IEEE INTERNATIONAL CONFERENCE ON BIG DATA SCIENCE AND ENGINEERING (IEEE BIGDATASE), 2018, : 1149 - 1156