Efficient and Privacy – Preserving Min and K-th Min Computations in Mobile Sensing Systems

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Efficient and Privacy-preserving Min and k-th Min Computations in Mobile Sensing Systems

Efficient and Privacy – Preserving Min and K-th Min Computations in Mobile Sensing Systems

Abstract

Protecting the privacy of mobile phone user participants is extremely important for mobile phone sensing applications. In Efficient and Privacy-Preserving Min and k-th Min Computations in Mobile Sensing Systems paper, we study how an aggregator can quickly calculate the minimum value or the kth minimum value of all user data without knowing it.We build two secure protocols using probabilistic coding schemes and a cipher system that allows for homomorphic bitwise XOR computations for our problems.

Introduction

Project on Efficient and Privacy-preserving Min and k-th Min Computations With the advance of information technology and modern manufacturing, huge numbers of smartphones equipped with CPUs, ROMs, and a variety of sensors such as GPS, accelerometer, camera, digital compass etc., have replaced outdated “dumb phones” and entered people’s lives. Smartphones are ubiquitous nowadays, and have excellent sensing, computing and communication capabilities. These advantages make the smartphone an outstanding carrier for mobile sensing jobs.

A large number of projects  that utilize smartphones to sense have emerged in recent years. All applications above demonstrate that technically a sensing job owner can outsource his or her job to a number of mobile phone users, collect the data sensed by these users, and then perform analyses on the aggregation of the data.

However, before we put any of these applications into practical use,Project on Efficient and Privacy-preserving Min and k-th Min Computations we still need to ask ourselves a very important question: are mobile phone users willing to give their sensed data to the job owner or the aggregator? One of the major factors that could cause a negative answer is the user’s privacy. Data acquired from a user’s smartphone may contain this user’s private information such as physical location, health condition, etc.

Consider, for example, a medical data sensing application that needs to continuously monitor users’ data. Clearly, these medical data needs to be protected with caution. Without reliable privacy protection, many users would hesitate to accept an invitation from such a mobile sensing application.

Advantages

  • Our protocols can be used by an untrusted aggregator to securely compute the minimum value or k-th minimum values in the aggregation of all users’ private data.
  • We rigorously define and prove the security of our two protocols in a standard cryptographic model.

Disadvantages

  • Data acquired from a user’s smartphone may contain this user’s private information such as physical location, health condition, etc
  • Most of them only consider how to let the aggregator compute the sum of users’ data securely.
  • The computation cost and communication cost are much higher.