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A survey on Privacy Preserving Data Aggregation Schemes in People Centric Sensing Systems and Wireless Domains

Affiliations

  • Department of Computer Science and Engineering, Kattankulathur, Chennai - 603203, Tamil Nadu, India
  • SRM University, Kattankulathur, Chennai - 603203, Tamil Nadu, India

Abstract


Background: The technological advances in mobile and wireless communication have paved way for a new era of large scale sensing network. Smart phones used by people are nowadays equipped with rich set of sensing capabilities. These mobile devices carried by people can be used together to form a network called people centric sensing network. Methods: Since people are the mobile custodians, privacy becomes a threat to the network. This paper focuses on a study about the various privacy preserving aggregation schemes existing in other domains of the wireless network including people centric sensing. The design goals and challenges that arise when attempting to protect the user’s sensitive data are analyzed. The techniques commonly used to achieve user’s privacy are the homomorphic property of encryption schemes, Data slicing and Mix schemes. Findings: A study about the existing privacy preserving aggregation schemes enables us to design a new method that is more suitable for people centric sensing network. The design considerations involved in developing a security and privacy framework are also identified. The privacy preserving aggregation scheme should support spatial and temporal aggregation with fault tolerance. The scheme has to be adaptable for both additive and non-additive statistical aggregation functions. Also maintaining the data integrity, data privacy and data accuracy with less computational and communication overhead is essential. Applications: Privacy protection is important for user’s participation which supports large set of cooperative sensing applications. Irrespective of various domains in wireless networks, the privacy preserving data aggregation model is discussed that is suitable for the context of the applications.

Keywords

Aggregation, People Centric Sensing System, Privacy Preserving, Smart Grid, Wireless Body Area Network, Wireless Sensor Network.

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References


  • Campbell AT, Eisenman SB, Lane ND. The rise of people-centric sensing. IEEE Internet Computing. 2008 Jul–Aug; 12(4):12–21.
  • Kapadia A, Kotz D, Triandopoulos D. Opportunistic sensing: Security challenges for the new paradigm. 2009 First International Communication Systems and Networks and Workshops; 2009 Jan. p. 1–10.
  • Campbell AT, Eisenman SB, Lane ND, Miluzzo E, Peterson RA. People-centric urban sensing. Proceedings ICST WICON; 2006.
  • Suveetha K, Manju T. Ensuring confidentiality of cloud data using homomorphic encryption. Indian Journal of Science and Technology. 2016 Feb; 9(8):1–7.
  • Padma, Rajalakshmi S. An efficient strategy to provide secure authentication on using TPM. Indian Journal of Science and Technology. 2015 Dec; 8(35):1–8.
  • Chen L, Lu R, Cao Z. MuDA: Multifunctional Data Aggregation in privacy-preserving smart grid communications. Peer-to-Peer Networking and Applications. 2015 Sep; 8(5):777–92.
  • Lu R, Liang X, Li X. Eppa: An efficient and privacy-preserving aggregation scheme for secure smart grid communications. IEEE Transactions on Parallel and Distributed Systems. 2012 Sep; 23(9):1621–31.
  • Chen L, Lu R, Cao Z. PDAFT: A privacy-preserving data aggregation scheme with fault tolerance for smart grid communications. Peer-to-Peer Networking and Application. 2015 Nov; 8(6):1122–32.
  • He W, Liu X, Nguyen H, Nahrstedt K, Abdelzaher TF. PDA: Privacy-preserving Data Aggregation in wireless sensor networks. Proceedings IEEE INFOCOM; 2007 May. p. 2045–53.
  • Shi J, Zhang Y, Liu Y. Prisense: privacy-preserving data aggregation in people-centric urban sensing systems. 2010 Proceedings IEEE INFOCOM; 2010 Mar. p. 1–9.
  • Zhang R, Shi J, Zhang Y. Verifiable privacy-preserving aggregation in people-centric urban sensing systems. IEEE Journal on Selected Areas in Communications. 2013 Sep; 31(9):268–78.
  • De Cristofaro E, Di Pietro R. Adversaries and countermeasures in privacy-enhanced urban sensing systems. IEEE Systems Journal. 2013 Jun; 7(2):311–22.
  • Li Q, Cao Q, La Porta T. Efficient and privacy-aware data aggregation in mobile sensing. Dependable and Secure Computing, IEEE Transactionson. 2013 Mar–Apr; 11(2):115–29.
  • Zhang K, Liang X, Baura M. PHDA: A Priority based Health Data Aggregation with privacy preservation for cloud assisted WBANs. Information Sciences. 2014 Nov; 284:130–41.
  • Han S, Zhao S, Li Q, Ju C-H, Zhou W. PPM-HDA: Privacy-preserving and multi-functional health data aggregation with fault tolerance. IEEE Transactions on Information Forensics and Security. 2016 Sep; 11(9):1940–55.

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