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

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.