We present a two-party secure information processing protocol referred to as SIPPA-2.0 - targeted towards privacy preserving biometric data comparison and reconstruction. The original intention of SIPPA as reported previously is to enable private data comparison and reconstruction between a client and a server when (a) the client possesses some data that are “sufficiently similar” to that of the server, and (b) the server provides a scalar helper data to facilitate private data reconstruction by the client. In SIPPA-2.0, private data comparison and reconstruction are based on new theoretical results and a novel secure computation protocol referred to as SLSSP. These new results allow us to design and develop the much improved SIPPA and SLSSP protocols guaranteeing (a) security under semi-malicious model rather than just semi-honest model, and (b) privacy assurance with arbitrary reconstruction accuracy controllable by the server. Security analysis proving SLSSP secure under the semi-honest and semi-malicious models is presented. SIPPA-2.0 is applied to enable privacy preserving fingerprint comparison; where two parties can compare their fingerprint samples and can obtain a similarity score without revealing their raw fingerprint to each other. Experimental results on the accuracy of fingerprint matching and the run-time performance are also reported.

This paper presents the design and development of a technique referred to as SIPPA - Secure Information Processing with Privacy Assurance - for biometric data reconstruction. SIPPA enables a client/server model with the following two properties: (1) the client party can compare the similarity between his/her sample data with the source data on the server side — without each party revealing his/her data to another, nor to a third party. If the sample data is “sufficiently similar” to the source data, the client can reconstruct the source data by using only the sample data and some helper data with negligible overhead provided by the server. The main contributions of this paper are: (1) algorithmic steps of SIPPA and its relationship to privacy homomorphism, (2) a parallel SIPPA architecture, and (3) the realization of parallel SIPPA as a service component for BioAPI 2.0 framework using Java RMI technology. To demonstrate its potential application, we apply SIPPA to the reconstruction of biometric data, and more specifically, biometric face images represented in terms of linearized vectors.

An intelligent communication device is being developed to assist the nonverbal, motor disabled in the generation of written and spoken messages. The device is based on a knowledge base of the grammatical rules and message elements. A 'belief' reasoning scheme makes use of highly uncertain speech-recognition information in combination with the embedded knowledge to optimize the process of message search. The authors focus on the integration of that speech information. Experimental testing of a prototype system to assess the effects of speech evidence is discussed.>

A frame control strategy is presented which directs an efficient graph search when information available for finding a path is distributed throughout the graph. The frame structure requires (worst case) only O(K/sup N/) memory size instead of O(K/sup 2N/), which is needed for a matrix representation of an N-level, K-branch tree graph. An efficiency analysis is detailed, and its application to a communication system for the nonverbal, severely motor disabled is discussed.>