## CryptoDB

#### Publications

Year
Venue
Title
2017
PKC
2016
PKC
2015
EPRINT
2015
EPRINT
2015
EPRINT
2014
EPRINT
2014
EPRINT
2006
EPRINT
We consider the problem of efficiently generating sequences in hardware for use in certain cryptographic algorithms. The conventional method of doing this is to use a counter. We show that sequences generated by linear feedback shift registers (LFSRs) can be tailored to suit the appropriate algorithms. For hardware implementation, this reduces both time and chip area. As a result, we are able to suggest improvements to the design of DES Cracker built by the Electronic Frontier Foundation in 1998; provide an efficient strategy for generating start points in time-memory trade/off attacks; and present an improved parallel hardware implementation of a variant of the counter mode of operation of a block cipher.
2006
EPRINT
In 1980, Hellman introduced a time/memory trade-off (TMTO) algorithm satisfying the TMTO curve $TM^2=N^2$, where $T$ is the online time, $M$ is the memory and $N$ is the size of the search space. Later work by Biryukov-Shamir incorporated multiple data to obtain the curve $TM^2D^2=N^2$, where $D$ is the number of data points. In this paper, we describe a new table structure obtained by combining Hellman's structure with a structure proposed by Oechslin. Using the new table structure, we design a new multiple data TMTO algorithm both with and without the DP method. The TMTO curve for the new algorithm is obtained to be $T^3M^7D^8=N^7$. This curve is based on a conjecture on the number of distinct points covered by the new table. Support for the conjecture has been obtained through some emperical observations. For $D>N^{1/4}$, we show that the trade-offs obtained by our method are better than the trade-offs obtained by the BS method.
2005
EPRINT
Time/memory trade-off (TMTO) was introduced by Hellman and later studied by many other authors. The effect of multiple data in Hellman TMTO was studied by Biryukov and Shamir (BS). We continue the analysis of the general multiple data TMTO started in BS. The trade-offs of Babbage and Golic (BG) and Biryukov-Shamir are obtained as special cases. Further, the general analysis is carried out under different conditions including that of Hellman optimality (online time equal to memory). Our main contribution is to identify a new class of single table multiple data trade-offs which cannot be obtained either as BG or BS trade-off. In certain cases, these new trade-offs can provide more desirable parameters than the BG or the BS methods. We consider the analysis of the rainbow method of Oechslin and show that for multiple data, the TMTO curve of the rainbow method is inferior to the TMTO curve of the Hellman method. The costs of the rainbow method and the Hellman+DP method can be comparable if the size of the search space is small and the cost of one table look-up is relatively high.

#### Coauthors

Pratish Datta (7)
Ratna Dutta (6)
Dibyendu Roy (1)
Palash Sarkar (3)