Multi-Party High-Dimensional Data Publishing under Differential Privacy

Multi-Party High-Dimensional Data Publishing under Differential Privacy

ABSTRACT:

In this paper, we study the problem of publishing high-dimensional data in a distributed multi-party environment under differential privacy. In particular, with the assistance of a semi-trusted curator, the parties (i.e., local data owners) collectively generate a synthetic integrated dataset while satisfying ε-differential privacy. To solve this problem, we present a differentially private sequential update of Bayesian network (DP-SUBN) approach. In DP-SUBN, the parties and the curator collaboratively identify the Bayesian network N that best fits the integrated dataset in a sequential manner, from which a synthetic dataset can then be generated. The fundamental advantage of adopting the sequential update manner is that the parties can treat the intermediate results provided by previous parties as their prior knowledge to direct how to learn N. The core of DP-SUBN is the construction of the search frontier, which can be seen as a priori knowledge to guide the parties to update N. By exploiting the correlations of attribute pairs, we propose exact and heuristic methods to construct the search frontier. In particular, to privately quantify the correlations of attribute pairs without introducing too much noise, we first put forward a non-overlapping covering design (NOCD) method, and then devise a dynamic programming method for determining the optimal parameters used in NOCD. Through privacy analysis, we show that DP-SUBN satisfies ε-differential privacy. Extensive experiments on real datasets demonstrate that DP-SUBN offers desirable data utility with low communication cost.

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS: 

  • System : Pentium Dual Core.
  • Hard Disk : 120 GB.
  • Monitor : 15’’ LED
  • Input Devices : Keyboard, Mouse
  • Ram : 1 GB

SOFTWARE REQUIREMENTS: 

  • Operating system : Windows 7.
  • Coding Language :
  • Tool : Netbeans 7.2.1
  • Database : MYSQL

REFERENCE:

Xiang Cheng, Peng Tang, Sen Su, Rui Chen, Zequn Wu and Binyuan Zhu, “Multi-Party High-Dimensional Data Publishing under Differential Privacy”, IEEE Transactions on Knowledge and Data Engineering, 2019.

Privacy Preserving Searchable Encryption with Fine-grained Access Control

Privacy Preserving Searchable Encryption with Fine-grained Access Control

ABSTRACT:

Searchable encryption facilitates cloud server to search over encrypted data without decrypting the data. Single keyword based searchable encryption enables a user to access a subset of documents, which contains the keyword of the user’s interest. In this paper, we present a single keyword based searchable encryption scheme for the applications where multiple data owners upload their data and then multiple users can access the data. The scheme uses attribute based encryption that allows user to access the selective subset of data from cloud without revealing his/her access rights to the cloud server. The scheme is proven adaptively secure against chosen-keyword attack in the random oracle model. We have implemented the scheme on Google cloud instance and the performance of the scheme found practical in real-world applications.

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS: 

  • System : Pentium Dual Core.
  • Hard Disk : 120 GB.
  • Monitor : 15’’ LED
  • Input Devices : Keyboard, Mouse
  • Ram : 1 GB

SOFTWARE REQUIREMENTS: 

  • Operating system : Windows 7.
  • Coding Language :
  • Tool : Netbeans 7.2.1
  • Database : MYSQL

REFERENCE:

Payal Chaudhari and Manik Lal Das, “Privacy Preserving Searchable Encryption with Fine-grained Access Control”, IEEE Transactions on Cloud Computing ( Early Access ), 2019.

 

Hybrid Keyword-Field Search with Efficient Key Management for Industrial Internet of Things

Hybrid Keyword-Field Search with Efficient Key Management for Industrial Internet of Things

ABSTRACT:

Equipped with the emerging cloud computing, clients prefer to outsource the increasing number of Industrial Internet of Things (IIoT) data to cloud to reduce the high storage and computation burden. However, existing Searchable Encryption (SE) schemes just apply to IIoT records containing textual keyword fields rather than both digital and textual keyword ones. Besides, the key management issue still impedes the practicality and availability of SE schemes due to high key storage overhead. To this end, we present an outsourced Hybrid Keyword-Field Search over encrypted data with efficient Keys Management (HKFS-KM) scheme by utilizing the relevance score function and keyed hash tree. Formal security analysis proves that the HKFS-KM scheme can achieve keyword privacy and trapdoor unlinkability in both known ciphertexts attack model and known background attack model. Experimental results using real-world dataset show its efficiency and practicality in practice.

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS: 

  • System : Pentium Dual Core.
  • Hard Disk : 120 GB.
  • Monitor : 15’’ LED
  • Input Devices : Keyboard, Mouse
  • Ram : 1 GB

SOFTWARE REQUIREMENTS: 

  • Operating system : Windows 7.
  • Coding Language :
  • Tool : Netbeans 7.2.1
  • Database : MYSQL

REFERENCE:

Yinbin Miao, Member, IEEE, Ximeng Liu, Robert H. Deng, Fellow, IEEE, Hongjun Wu, Hongwei Li, Jiguo Li, and Dapeng Wu, “Hybrid Keyword-Field Search with Efficient Key Management for Industrial Internet of Things”, IEEE Transactions on Industrial Informatics, Volume: 15 , Issue: 6 , June 2019.