Informatics Publishing Limited

Archana R. Kulkarni ¹, Andhe Pallavi ²

Affiliations
1 EC Dept, RNSIT, Bangalore, IN
2 IT Dept, RNSIT, Bangalore, IN

Abstract

Iterative multi user detection (MUD) enables optimal data detection and decoding in communication channels by interacting between multiuser detection and channel decoding [11-13]. Increasing number of active users in the multiple-access channel increases the interference. To minimize the degradation, the number of iterations is usually increased at the cost of additional latency. In this paper it is shown that a slight modification in the implementation of message authentication codes (MAC) in the iterative MUD leads to a decrease in the number of iterations required for optimal detection and also improves the performance of the multiuser receiver.

In this paper the iterative MUD has the MAC bits spread out in the convolutional coded data block. These uncoded MAC bits are used as apriori data bits in the iterative decoding. This results in a reduction of the number of iterations required for the same BER. An EXIT chart of the combination of the convolutional decoder block with that of the multi user detector is created and the BER results are validated.

 

Keywords

Bit Error Rate (BER) and EXIT Chart, Iterative MUD, Message Authentication Code (MAC).

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Ganesh Yernally ¹, Andhe Pallavi ²

Affiliations
1 RNS Institute of Technology, Bangalore, IN
2 Dept. of IT, RNS Institute of Technology, Bangalore, Karnataka, IN

Abstract

Nowadays, the collection of personal data by research organizations and sharing of this data with other organization for business intensions has been increased tremendously. Medical data of individuals is most sensitive among other shared private data. Although some specific values like names and id numbers etc. are removed from shared data to protect the individual privacy the medical data released to research organizations is still susceptible to linking attack which can compromise the patients’ privacy. To prevent linking attack k-Anonymity Property is used. In a k-anonymized dataset, each record is indistinguishable from at least k−1 other records with respect to certain ―identifying‖ attributes. As k-anonymity cannot prevent attribute disclosure, to go beyond k-anonymity the notion of l-diversity is used to address this; l-diversity requires that each equivalence class has at least l well-represented values for each sensitive attribute. In this paper we implemented a system using VB.Net for k-anonymous and distinct l-diverse table generation. We propose two algorithms K-Gen and L-Gen for the system implementation with generalization index values. The generalization index values controls the levels of generalization for each ―identifying‖ attribute. Experimental results show that the proposed algorithms have lesser Discernibility value and comparable information loss as compared with existing methods. The system implemented in this paper protects patients’ identity by efficient implementation of k-anonymity and distinct l-diversity properties, providing faster data release with intuitive GUI. It also offers a cost-effective solution to patients’ data holding organizations, as they need not buy any proprietary black boxes to protect the released data.

Keywords

Generalization Index Values (GIV), K-Gen, L-Gen.

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