G
George I. Davida
Researcher at University of Wisconsin–Milwaukee
Publications - 52
Citations - 1761
George I. Davida is an academic researcher from University of Wisconsin–Milwaukee. The author has contributed to research in topics: Encryption & Cryptography. The author has an hindex of 15, co-authored 52 publications receiving 1731 citations. Previous affiliations of George I. Davida include University of Wisconsin-Madison & University of Iowa.
Papers
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Proceedings ArticleDOI
On enabling secure applications through off-line biometric identification
TL;DR: This paper studies secure off-line authenticated user identification schemes based on a biometric system that can measure a user's biometrics accurately (up to some Hamming distance) and investigates a new technology which allows a users' biometric data to facilitate cryptographic mechanisms.
Journal ArticleDOI
A database encryption system with subkeys
TL;DR: A new cryptosystem that is suitable for database encryption is presented, based on the Chinese Remainder Theorem, which has the important property of having subkeys that allow the encryption and decryption of fields within a record.
Book ChapterDOI
Hiding the Hidden: A software system for concealing ciphertext as innocuous text
Mark Chapman,George I. Davida +1 more
TL;DR: A system for protecting the privacy of cryptograms to avoid detection by censors is presented, which transforms ciphertext into innocuous text which can be transformed back into the original ciphertext.
Proceedings ArticleDOI
Defending systems against viruses through cryptographic authentication
TL;DR: In this paper, the authors describe the use of cryptographic authentication for controlling computer viruses, which relies on a trusted device, the authenticator, used to authenticate and update programs and convert programs between the various formats.
Book ChapterDOI
A Practical and Effective Approach to Large-Scale Automated Linguistic Steganography
TL;DR: This paper expands on a previous approach that used sentence models and large dictionaries of words classified by part-of-speech by using an "extensible contextual template" approach combined with a synonymbased replacement strategy, much more realistic text is generated than was possible with NICETEXT.