Challenges of Privacy-Preserving OLAP Techniques

被引:0
|
作者
Gorlatykh, Andrey V. [1 ]
Zapechnikov, Sergey V. [1 ]
机构
[1] Natl Res Nucl Univ MEPhI, Moscow Engn Phys Inst, Dept Cryptol & Cybersecur, Moscow, Russia
关键词
On-line Analytical Processing (OLAP); information security; privacy; partly homomorphic encryption;
D O I
暂无
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Over the last five years, on-line analytical processing (OLAP) became one of the essential information processing technologies. OLAP technology has been successfully used in different areas: retail, financial services, telecommunication, health care etc. Because of this security of data stored in Data Warehouses became one of the most important aspect of this technology, especially when we speaking about data privacy. We review existing privacy-preserving OLAP techniques and identify new challenges of this technology. In particular, OLAP databases are placed often in the untrusted clouds, so it is crucial to create techniques for evaluating widely used statistical functions (mean value, standard deviation, minimum, maximum, and so on) over the encrypted data. For this purpose, we review and compare encryption schemes with special features (partly homomorphic, order-preserving, deterministic etc.) and suggest architecture of application for private OLAP over the encrypted database.
引用
收藏
页码:404 / 408
页数:5
相关论文
共 50 条
  • [21] Privacy-Preserving Tools and Technologies: Government Adoption and Challenges
    Prabowo, Sidik
    Putrada, Aji Gautama
    Oktaviani, Ikke Dian
    Abdurohman, Maman
    Janssen, Marijn
    Nuha, Hilal Hudan
    Sutikno, Sarwono
    IEEE ACCESS, 2025, 13 : 33904 - 33934
  • [22] Privacy-Preserving Audience Measurement in Practice - Opportunities and Challenges
    Passmann, Steffen
    Lauber-Roensberg, Anne
    Strufe, Thorsten
    2017 IEEE CONFERENCE ON COMMUNICATIONS AND NETWORK SECURITY (CNS), 2017, : 444 - 449
  • [23] EPPD: Efficient and Privacy-Preserving Proximity Testing with Differential Privacy Techniques
    Huang, Cheng
    Lu, Rongxing
    Zhu, Hui
    Shao, Jun
    Alamer, Abdulrahman
    Lin, Xiaodong
    2016 IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC), 2016,
  • [24] Privacy-Preserving Mechanisms for Crowdsensing: Survey and Research Challenges
    Vergara-Laurens, Idalides J.
    Jaimes, Luis G.
    Labrador, Miguel A.
    IEEE INTERNET OF THINGS JOURNAL, 2017, 4 (04): : 855 - 869
  • [25] Towards Privacy-Preserving Deep Learning: Opportunities and Challenges
    Ali, Sheraz
    Irfan, Muhammad Maaz
    Bomai, Abubakar
    Zhao, Chuan
    2020 IEEE 7TH INTERNATIONAL CONFERENCE ON DATA SCIENCE AND ADVANCED ANALYTICS (DSAA 2020), 2020, : 673 - 682
  • [26] Privacy-Preserving Machine Learning as a Service: Challenges and Opportunities
    Zhang, Qiao
    Xiang, Tao
    Cai, Yifei
    Zhao, Zhichao
    Wang, Ning
    Wu, Hongyi
    IEEE NETWORK, 2023, 37 (06): : 214 - 223
  • [27] Comparative Analysis of Privacy-Preserving Data Mining Techniques
    Bhandari, Neetika
    Pahwa, Payal
    INTERNATIONAL CONFERENCE ON INNOVATIVE COMPUTING AND COMMUNICATIONS, VOL 2, 2019, 56 : 535 - 541
  • [28] Privacy-preserving techniques of genomic data-a survey
    Al Aziz, Md Momin
    Sadat, Md Nazmus
    Alhadidi, Dima
    Wang, Shuang
    Jiang, Xiaoqian
    Brown, Cheryl L.
    Mohammed, Noman
    BRIEFINGS IN BIOINFORMATICS, 2019, 20 (03) : 887 - 895
  • [29] Efficient Techniques for Privacy-Preserving Sharing of Sensitive Information
    De Cristofaro, Emiliano
    Lu, Yanbin
    Tsudik, Gene
    TRUST AND TRUSTWORTHY COMPUTING, TRUST 2011, 2011, 6740 : 239 - 253
  • [30] Modern Privacy-Preserving Record Linkage Techniques: An Overview
    Gkoulalas-Divanis, Aris
    Vatsalan, Dinusha
    Karapiperis, Dimitrios
    Kantarcioglu, Murat
    IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, 2021, 16 : 4966 - 4987