CFP last date
20 March 2024
Reseach Article

Discrimination Aware Data Mining in Internet of Things (IoT)

by Asmita Gorave, Vrushali Kulkarni
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 159 - Number 3
Year of Publication: 2017
Authors: Asmita Gorave, Vrushali Kulkarni

Asmita Gorave, Vrushali Kulkarni . Discrimination Aware Data Mining in Internet of Things (IoT). International Journal of Computer Applications. 159, 3 ( Feb 2017), 39-42. DOI=10.5120/ijca2017912894

@article{ 10.5120/ijca2017912894,
author = { Asmita Gorave, Vrushali Kulkarni },
title = { Discrimination Aware Data Mining in Internet of Things (IoT) },
journal = { International Journal of Computer Applications },
issue_date = { Feb 2017 },
volume = { 159 },
number = { 3 },
month = { Feb },
year = { 2017 },
issn = { 0975-8887 },
pages = { 39-42 },
numpages = {9},
url = { },
doi = { 10.5120/ijca2017912894 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-07T00:04:47.343570+05:30
%A Asmita Gorave
%A Vrushali Kulkarni
%T Discrimination Aware Data Mining in Internet of Things (IoT)
%J International Journal of Computer Applications
%@ 0975-8887
%V 159
%N 3
%P 39-42
%D 2017
%I Foundation of Computer Science (FCS), NY, USA

IoT is a technology where objects around us will be able to connect to each other and communicate via Internet. Recently, IoT has become an important technology in the world of Internet. However, it comes across problem of big data and extracting knowledge from such data using data mining techniques. Recently, it is observed that data mining increases the risk of violation of fundamental human right, called non-discrimination. It is obvious that data mining tasks in IoT will also face the risk of discrimination. Discrimination Aware Data Mining is an area which deals with finding methods to discover and/or prevent discrimination. This paper is the first step towards finding discrimination related issues in IoT. This paper describes discrimination discovery and prevention issues faced by IoT. This paper also specifies the huge future research avenue related to discrimination aware data mining in IoT.

  1. C. Perera, A. Zaslavsky, P. Christen, and D. Georgakopoulos, “Context-aware computing for the Internet of Things : a survey,” IEEE Communications Surveys & Tutorials, submitted 2013.
  2. John A. Stankovic, “Research Directions for the Internet of Things,” IEEE Internet of Things Journal, vol.1, no.1, pp. 3-9, February, 2014.
  3. D. Miorandi, S. Sicari, F. De Pellegrini, and I. Chlamtac, “Internet of things: vision, application and research challenges,” Ad Hoc Networks, vol.10, no. 7, pp. 1497-1516, 2012.
  4. Chun-Wei Tsai, Chin-Feng Lai, Ming-Chao Chiang and Laurence T. Yang, “Data Mining for Internet of Things: A Survey,” IEEE Communications Surveys & Tutorials, vol. 16, no.1, pp. 77-97, 2014.
  5. J. Gubbi, R. Buyya, S. Marusic, and M. Palaniswami, “Internet of Things (IoT): A vision, architectural elements, and future directions,” Future Generation Computer Systems, vol. 29, no.7, pp. 1645-1660, 2013.
  6. A. S. Elmaghraby and M. M. Losavio, “Cyber security challenges in Smart Cities: Safety, security and privacy,” J. Adv. Res., vol. 5, no.4, pp. 491-497, 2014.
  7. S. Sicari, A. Rizzardi, L.A Grieco and A. Coen-Porisini, “Security, privacy and trust in Internet of Things: The road ahead,” Comput. Netw. 76, 146-164, 2015.
  8. D. Pedreschi, S. Ruggieri, and F. Turini, “Discrimination-Aware Data Mining,” Proc. 14th ACM Int’l Conf. Knowledge Discovery and Data Mining (KDD ’08), pp. 560-568, 2008.
  9. S. Ruggieri, D. Pedreschi, and F. Turini, “Data Mining for Discrimination Discovery,” ACM Trans. Knowledge Discovery from Data, vol. 4, no. 2, article 9, 2010.
  10. S. Hajian & J. Domingo-Ferrer, “A Methodology for Direct and Indirect Discrimination prevention in data mining,” IEEE transaction on knowledge & data engg., pp. 1445-1459, 2013.
  11. F. Kamiran and T. Calders, “Data preprocessing techniques for classification without discrimination,” Intl’ Journal of Knowledge & Information Systems, Springer, Vol.33, no.1, pp. 1-33, 2012.
  12. F. Kamiran, T. Calders, and M. Pechenizkiy, “Discrimination Aware Decision Tree Learning,” Proc. IEEE Int’l Conf. Data Mining (ICDM ’10), pp. 869-874, 2010.
  13. T. Calders and S. Verwer, “Three Naive Bayes Approaches for Discrimination-Free Classification,” Data Mining and Knowledge Discovery, vol. 21, no. 2, pp. 277-292, 2010.
  14. D.Pedreschi, S.Ruggieri and F.Turini, “Measuring Discrimination in Socially-Sensitive Decision Records,” Proc. Ninth SIAM Data Mining Conf. (SDM ’09), pp. 581-592, 2009.
  15. Frieder Ganz, Daniel Puschmann, Payam Barnaghi and Francois Carrez, “A Practical Evaluation of Information Techniques for the Internet of Things,” IEEE Internet of Things Journal, vol.2, no.4, pp. 340- 354, 2015.
  16. L. Wang, “Using the relationship of shared neighbors to find hierarchical overlapping communities for effective connectivity in IoT,” in Proc. International Conference on Pervasive Computing and Applications, pp. 400–406, 2011.
Index Terms

Computer Science
Information Sciences


Discrimination aware data mining data mining Internet of Things (IoT)