iosr-JCE   Volume-9 ~ Issue-2

Abstract: Voice over Internet Protocol (VoIP) is a technology that enables one to make and receive calls through the Internet instead of using the traditional analog PSTN (Public Switched Telephone Network) lines. Gtalk and Skype are frequently used for this purpose, which is a third party. So, we are utilizing their environment for conferencing. We are in need of a server to transfer the information from source to destination i.e. we are compromising our privacy at a certain level as they do not provide full security to our audio packets. Here, we do propose, novel flow analysis attacks that ensures both privacy and demonstrates the vulnerabilities in peer-to-peer VoIP networks. Solutions are proposed by quantifiable k-anonimity metrices.

Keywords: Flow analysis attacks, k-anonymity, mix networks, privacy, VoIP networks

1] Mudhakar Srivatsa, Arun Lyengar,Ling liu, Hongbo jiang ―Privacy in VoIP Networks: Flow Analysis Attacks and
Defense ―IEEE20ll.
[2] ―The Network Simulator NS-2,‖ http://www.isi.edu/nsnam/ns/,2010.
[3] ―The Network Simulator NS-2: Topology Generation,‖ http://www.isi.edu/nsnam/ns/ns-topogen.html , 2010'
[4] Phex Client,‖ http://www.phex.org, 2010
[5] ―Skype—The Global Internet Telephone Company,‖ http://www.skype.com, 2010.
[6] ―Telegeography Research,‖ http://www.telegeography.com, 2010.
[7] GT-ITM: Georgia, Tech Internetwork Topology Models,‖ http://www.cc.gatech.edu/projects/gtitm/ , 2010.
[8] M.J. Freedman and R. Morris, ―Tarzan: A Peer-to-Peer Anonymizing Network Layer,‖ Proc. Ninth ACM Conf. Computer and
Comm. Security (CCS), 2002.
[9] R. Dingledine, N. Mathewson, and P. Syverson, ―Tor: The Second Generation Onion Router,‖ Proc. 13th USENIX Security Symp., 2000.
[10] C# Network Programming book by Richard Blum.

Abstract: Location management refers to the problem of updating and searching the current locations of mobile nodes in a wireless network. The location administration basically is performed by using two registers, HLR (Home Location Register) and VLR (Visitor Location Register). The current location information has to be maintained efficiently in location registers for effective call delivery. In the system it is difficult to deliver a call to the users at an optimal time interval. This is used to manage and reduce the level of time of call forwarding from the caller to the callee. To make it efficient, the sum of update/lookup costs of a location database must be minimized. To reduce the cost of location database, this paper we are using the concept of time based location administration system. This paper proposed the CACHE scheme which register the user's frequent movement of repeated visit of same location and maintain the user's detail up-to-date. This optimizes the time-interval which minimize the location administration cortical factors to be established

1] Bjorn Landfeldt, "A Dynamic Location management on Scheme based on individual metrics and coordinates" IEEE Global
Telecommunications Conference, Vol. 3, pp. 1426–1430, 2006.
[2] Jie Li, Yi Pan and Yang Xiao, "A Dynamic HLR Location management Scheme for PCS", IEEE.
[3] Kwok, YK, "A quasi-static cluster-computing approach for dynamic channel assignment in cellular mobile communication systems"
IEEE 10.1109/VETECF.1999.797357 1999.
[4] Munadi, R.; Ismail, M.; Abdullah, M.; Misran, N.; Electr. Eng. Dept., Syiah Kuala Univ., Banda Aceh, Indonesia," Location
management cost reduction using fuzzy logic in cellular radio network", Space Science and Communication (IconSpace), 2011 IEEE
International Conference.
[5] Networks" IEEE Trans. 0-7803-8356-7/04/$20.00 IEEE INFOCOM 2004.
[6] Rodriguez-Dagnino, R.M.; Takagi, H.;
Monterrey Inst. of Technol. & Higher Educ. (ITESM), Monterrey," Movement-Based Location management for General Cell
Residence Times in Wireless Networks", IEEE Vehicular Technology Society, 2007.
[7] Travis Keshav "Location management in Wireless Cellular Networks" IEEE Trans. 2005: v.54, no.2 687-708.
[8] Vincent W S. Wong and Victor C. M. Leung, "Location management for Next Generation Personal Communication Networks"
Paper accepted by IEEE Network, Special Issue on Next Generation Wireless Broadband Networks.
[9] Yong Lee, "Optimal Time-Interval for Time-based Location Update in Mobile Communications"0-7803-7632-3/02/$17.000 2002
IEEE.
[10] S. Ratnasamy, B. Karp, L. Yin, F. Yu, D. Estrin, R. Govindan, and S. Shenker. GHT: a geographic hash table for data-centric
storage. In ACM Int'l workshop on Wireless Sensor Networks and Applications (WSNA), pages 78–87, 2002.

Abstract: Data cleaning is an automated process of detecting, removing and correcting incomplete, incorrect, inaccurate and irrelevant data from a record set. Our system works on simple text (*.txt) files using Extract, Transform and Load (ETL) model. In this paper we present a set of algorithms to correct errors such as alphanumeric errors, invalid gender, invalid ID pattern and redundant ID error. The text files are used as data storage which stores data in a tabular format and the algorithms are applied on each field value depending on its nature.

Keywords - ETL, Dirty Data, ID Validation, Alphabetic Validation, Numeric Validation

[1] R. Cody, "Data cleaning 101," Proceedings for the Twenty-Seventh SAS User Group International Conference. Cary, NC: SAS
Institute Inc,2000.
[2] Dr. Mortadha M. Hamad and Alaa Abdulkhar Jihad, "An Enhanced Technique to Clean Data in the Data Warehouse". Computer
Science Department. University of Anbar, Ramadi, Iraq.
[3] Hasimah Hj Mohamed, Tee Leong Kheng, Chee Collin and Ong Siong Lee, "E-Clean: A Data Cleaning Framework for Patient Data".
School of Computer Sciences. University Sains Malaysia Penang, Malaysia.
[4] Arindam Paul, Varuni Ganesan, Jagat Sesh Challa and Yashvardhan Sharma, "HADCLEAN: A Hybrid Approach to Data Cleaning in
Data Warehouses". Department of Computer Science & Information Systems . Birla Institute of Technology & Science, Pilani,
Rajasthan, India – 333031.
[5] Erhard Rahm and Hong Hai Do. "Data Cleaning: Problems and Current Approaches". University of Leipzig, Germany.
[6] Srivatsa Maddodi, Girija V. Attigeri and Dr. Karunakar A. K, "Data Deduplication Techniques and Analysis". Manipal Institute of
Technology, Manipal, India.
[7] R. Kimball and J. Caserta, "The Data Warehouse ETL Toolkit". Wiley,2004.
[8] V. Raman and J. M. Hellerstein, "Potter‟s Wheel: An Interactive Framework for Data Transformation and Cleaning.," in Proceedings
of the 27th VLDB Conference, Roma, Italy, 2001.
[9] K. Kukich, "Techniques for Automatically Correcting Words in Text", ACM Computing Surveys, vol. 24, no. 4, pp.377 -439, 1992.
[10] R. Bheemavaram, J. Zhang and W. N. Li, "Efficient Algorithms for Grouping Data to Improve Data Quality", roceedings of the 2006 International Conference on Information & Knowledge Engineering (IKE 2006), CSREA Press, Las Vegas, Nevada, USA, pp. 149-
154, 2006.