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Abstract. The importance of privacy cannot be emphasized too much. That is true even for reporting exam scores or sending individual comments in the school. Any sensitive data including students" exam scores should be kept confidentially and protected from any kinds of attacks against privacy. If the online management system is well-equipped and securely managed in the school, then it is easy to distribute individual reports. However, such a system costs high and does not support flexible and dedicated formats designed for each purpose. In this paper, we propose a practical way for an instructor to post students" individual exam scores in a single file. By implementing a cryptographic hash function together with score data in an MS Excel file, we demonstrate a score report from which allows each student to retrieve his/her score in a privacy preserving way based on the password. Since our worksheet runs cryptographic operations with only built-in functions of Excel, it is easy for instructors to write their own score reports based on ours without relying on any management systems. Also, it is cryptographically as secure as underlying hash function SHA-256.

Abstract. When random numbers are needed, kernel level threads can use at least one of two random number generators (RNGs), called LRNG and MD5 PRNG. LRNG is a well-known cryptographic RNG accessed via /dev/(u)random and MD5 PRNG provides a function interface get_random_int(). While the /dev/(u)random has been investigated a lot, MD5 PRNG had been regarded as a less important one. In this paper, we demonstrate MD5 PRNG is vulnerable against a generic attack by searching entropy source in some embedded systems. In fact, once a random number is obtained, one can predict previous outputs of MD5 PRNG. Even though the RNG uses high resolution clock of microsecond unit as an entropy source, we can recover random number by reducing search space. We suggest a generic attack which the attacker just guesses entropy source at most 2 53 times. This attack can be done within 74 hours using parallel implementation with NVIDIA GeForce Titan X.

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