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Karthik, Saritha
- Secure Aggregation against Collusion Attacks and Compromised Aggregator
Authors
1 International Graduate Studies College, BN
Source
Indian Journal of Innovations and Developments, Vol 4, No 8 (2015), Pagination: 1-9Abstract
Objective: To provide Secure Aggregation against Collusion attacks and Malicious or compromised aggregator.
Statistical Analysis: Wireless Sensor Network incorporate aggregation of data from multiple sensor nodes performed at the aggregator node or the cluster head due to limited computational power and energy. This aggregation technique is vulnerable to attacks of compromised nodes. Thus a secure Data Aggregation for Collusion Attacks (SACA) was proposed earlier that improved the previously stated IF algorithms by providing initial approximation of trustworthiness of the sensor nodes. This made the algorithm to be more accurate and faster.
Findings: The problem in this approach is that these improvisations were made with the assumption that the aggregator is not compromised. So in case if the aggregator node is compromised this method stands pointless in determining the security. Thus a new framework called Secure aggregation against Collusion Attacks and compromised aggregator (SACACA) is proposed to ensure that the Secure Date Aggregation scheme also fetch protection over the compromised aggregator node. The proposed work initializes with an aggregator node, that estimates the error and noise of the other sensor nodes in the cluster, and then calculates the reputation vector of each node and provides the information of trustworthiness of each node to the enhanced IF algorithm. The aggregator sends the aggregated information to the base station directly or to the aggregator of another cluster's aggregator and then reaches to the base station. This procedure is repeated by the replacement of the aggregator by a node in the cluster as the next aggregator node once in a time period. Thus the path of the aggregated information to the base station may vary according to the selection of the aggregator in each cluster. The Variance of each aggregator is calculated in the Base Station. The Calculated values are compared to a threshold value that determines whether the aggregator node is compromised or not. In order to find the variance value the enhanced IFs algorithm is utilized.
Improvements: Thus the proposed method improves the effectiveness of the enhanced IF algorithm over the compromised aggregator nodes. The change of aggregator in the network also saves a significant amount of computational power and energy.
Keywords
Wireless Sensor Networks, Secure Data Aggregation, Collusion Attacks, Compromised Aggregator.- Privacy Preserving Protocol Using K-Nearest Neighbor Algorithm for Cloud Based E-Healthcare Systems
Authors
1 International Graduate Studies College, BN
Source
Indian Journal of Education and Information Management, Vol 5, No 2 (2016), Pagination: 1-8Abstract
Background/Objectives: To develop a kNN-privacy preserving model for preserving the privacy of the patients in a cloud assisted e-healthcare system as the sensitive information is needed to be maintained confidential and should not be revealed to public users other than the physicians.
Methods/Statistical analysis: PPDM uses a privacy-preserving fully Homomorphic data aggregation as the basic scheme. The outsourcing of disease modeling and the early intervention is performed by developing privacypreserving function correlation matching PPDM1 from dynamic medical text mining and also a privacy-preserving medical image feature extraction PPDM2. Both PPDM1 and PPDM2 provides higher security level with reduced cipher text attach possibility and minimal overhead. Though the computational and communication overhead are reduced in PPDM, the use of correlation function threshold in PPDM1 can further be improved by utilizing an efficient machine learning algorithm. Hence, the simplest and efficient machine learning algorithm, k-nearest neighbor is utilized to develop kNN-PPP model.
Findings: In kNN-PPP model, instead of using correlation function threshold based matching, secure squared Euclidean distance of encrypted personal data and encrypted physician template is determined and then matched with better probability. Secure squared Euclidean distance protocol and secure multiplication protocols are the most prominent protocols among those utilized in kNN-PPP model.
Improvements/Applications: Using kNN-PPP protocols, the computation and communication overheads are also reduced considerably than the PPDM model for the better health status determination of the patients. Experimental results also show that the kNN-PPP model has minimized overheads and higher matching probability.
Keywords
Privacy Preserving, Homomorphic Data Aggregation, K-Nearest Neighbor, Secure Squared Euclidean Distance.References
- Jun Zhou, Zhenfu Cao, Xiaolei Dong, Xiaodong Lin. PPDM: A Privacy-Preserving Protocol for Cloud-Assisted eHealthcare Systems. Selected Topics in Signal Processing, IEEE Journal of 2015; 9(7), 1332-1344.
- Yousef Elmehdwi, Bharath K. Samanthula, Wei Jiang. Secure k-nearest neighbor query over encrypted data in outsourced environments. In Data Engineering (ICDE), 2014 IEEE 30th International Conference on.IEEE. 2014; 664-675.
- Huang Qin-Long, Yi-xian YANG, Jing-yi FU, Xin-xin NIU. Secure and privacy-preserving DRM scheme using Homomorphic encryption in cloud computing. The Journal of China Universities of Posts and Telecommunications. 2013; 20(6), 88-95.
- Kuan Zhang, Xiaohui Liang, MrinmoyBaura, Rongxing Lu, Xuemin Sherman Shen. PHDA: A priority based health data aggregation with privacy preservation for cloud assisted WBANs.Information Sciences. 2014; 284, 130-141.
- Soohyung Kim, Min Kyoung Sung, Yon Dohn Chung. A framework to preserve the privacy of electronic health data streams. Journal of biomedical informatics. 2014; 50, 95-106.
- Le Chen, Rongxing Lu, Zhenfu Cao, Khalid AlHarbi, Xiaodong Lin. MuDA: Multifunctional data aggregation in privacy-preserving smart grid communications. Peer-to-peer networking and applications. 2015; 8(5), 777-792.
- Meiyu Huang, Yiqiang Chen, Bo-Wei Chen, Junfa Liu, Seungmin Rho, Wen Ji. A semi-supervised privacy-preserving clustering algorithm for healthcare. Peer-to-Peer Networking and Applications. 2015; 1-12.
- Cheng Qian, Jian Wang. Secure and Efficient Protocol for Outsourcing Large-Scale Systems of Linear Equations to the Cloud. In Cloud Computing and Security. Springer International Publishing. 2015, 25-37.
- Wei Wang, Lei Chen, Qian Zhang. Outsourcing high-dimensional healthcare data to cloud with personalized privacy preservation.Computer Networks.2015; 88, 136-148.
- Ji-Jiang Yang, Jian-Qiang Li, Yu Niu. A hybrid solution for privacy preserving medical data sharing in the cloud environment. Future Generation Computer Systems.2015; 43, 74-86.
- P.B. Prince, K. Krishnamoorthy, R. Anandaraj, S.J. Lovesum. RSA-DABE: A Novel Approach for Secure Health Data Sharing in Ubiquitous Computing Environment. Indian Journal of Science and Technology. 2015; 21;8(17), 1-9.
- M. Janaki, Dr.M.GanagaDurga. Preserving sensitive data shared through the network against security threats using cryptography. Indian Journal of Education and Information Management, 2015; 4 (1), 1-5.