Toward a Statistical Framework for Source Anonymity in Sensor Networks
In certain applications, the locations of events reported by a sensor network need to remain anonymous. That is, unauthorized observers must be unable to detect the origin of such events by analyzing the network traffic. Known as the source anonymity problem, this problem has emerged as an important topic in the security of wireless sensor networks, with variety of techniques based on different adversarial assumptions being proposed. In this work, we present a new framework for modeling, analyzing, and evaluating anonymity in sensor networks. The novelty of the proposed framework is twofold: first, it introduces the notion of “interval indistinguishability” and provides a quantitative measure to model anonymity in wireless sensor networks; second, it maps source anonymity to the statistical problem of binary hypothesis testing with nuisance parameters. We then analyze existing solutions for designing anonymous sensor networks using the proposed model. We show how mapping source anonymity to binary hypothesis testing with nuisance parameters leads to converting the problem of exposing private source information into searching for an appropriate data transformation that removes or minimize the effect of the nuisance information. By doing so, we transform the problem from analyzing real-valued sample points to binary codes, which opens the door for coding theory to be incorporated into the study of anonymous sensor networks. Finally, we discuss how existing solutions can be modified to improve their anonymity.
PROJECT OUTPUT VIDEO: (Click the below link to see the project output video):
While transmitting the “description” of a sensed event in a private manner can be achieved via encryption primitives, hiding the timing and spatial information of reported events cannot be achieved via cryptographic means.
Encrypting a message before transmission, for instance, can hide the context of the message from unauthorized observers, but the mere existence of the ciphertext is indicative of information transmission.
In the existing literature, the source anonymity problem has been addressed under two different types of adversaries, namely, local and global adversaries. A local adversary is defined to be an adversary having limited mobility and partial view of the network traffic. Routing based techniques have been shown to be effective in hiding the locations of reported events against local adversaries.
A global adversary is defined to be an adversary with ability to monitor the traffic of the entire network (e.g., coordinating adversaries spatially distributed over the network). Against global adversaries, routing based techniques are known to be ineffective in concealing location information in event-triggered transmission. This is due to the fact that, since a global adversary has full spatial view of the network, it can immediately detect the origin and time of the event-triggered transmission
DISADVANTAGES OF EXISTING SYSTEM:
The source anonymity problem in wireless sensor networks is the problem of studying techniques that provide time and location privacy for events reported by sensor nodes. (Time and location privacy will be used interchangeably with source anonymity throughout the paper.)
The source anonymity problem has been drawing increasing research attention recently.
In this paper, we investigate the problem of statistical source anonymity in wireless sensor networks. The main contributions of this paper can be summarized by the following points.
We introduce the notion of “interval in-distinguishability” and illustrate how the problem of statistical source anonymity can be mapped to the problem of interval indistinguishability.
We propose a quantitative measure to evaluate statistical source anonymity in sensor networks.
We map the problem of breaching source anonymity to the statistical problem of binary hypothesis testing with nuisance parameters.
We demonstrate the significance of mapping the problem in hand to a well-studied problem in uncovering hidden vulnerabilities. In particular, realizing that the SSA problem can be mapped to the hypothesis testing with nuisance parameters implies that breaching source anonymity can be converted to finding an appropriate data transformation that removes the nuisance information.
We analyze existing solutions under the proposed model. By finding a transformation of observed data,we convert the problem from analyzing real-valued samples to binary codes and identify a possible anonymity breach in the current solutions for the SSA problem.
We pose and answer the important research question of why previous studies were unable to detect the possible anonymity breach identified in this paper.
We discuss, by looking at the problem as a coding problem, a new direction to enhance the anonymity of existing SSA solutions.
ADVANTAGES OF PROPOSED SYSTEM:
Removes or minimize the effect of the nuisance information
üProcessor – Pentium –IV
üSpeed – 1.1 Ghz
üRAM – 256 MB(min)
üHard Disk – 20 GB
üKey Board – Standard Windows Keyboard
üMouse – Two or Three Button Mouse
üMonitor – SVGA
üOperating System : Windows XP
üProgramming Language : JAVA
üJava Version : JDK 1.6 & above.
Basel Alomair, Member, IEEE, Andrew Clark, Student Member, IEEE, Jorge Cuellar, and Radha Poovendran, Senior Member, IEEE, “Toward a Statistical Framework for Source Anonymity in Sensor Networks”, IEEE TRANSACTIONS ON MOBILE COMPUTING, VOL. 12, NO. 2, FEBRUARY 2013.