Trade-Off between Collusion Resistance and User Life Cycle in Self-Healing Key Distributions with t-Revocation

We solve the problem of resisting the collusion attack in the one-way hash chain based self-healing key distributions introduced by Dutta et al., coupling it with the prearranged life cycle based approach of Tian et al. that uses the same self-healing mechanism introduced in Dutta et al. Highly efficient schemes are developed compared to the existing works with the trade-off in pre-arranged life cycles on users by the group manager and a slight increase in the storage overhead. For scalability of business it is often necessary to design more innovation and flexible business strategies in certain business models that allow contractual subscription or rental, such as subscription of mobile connection or TV channel for a pre-defined period. The subscribers are not allowed to revoke before their contract periods (life cycles) are over. Our schemes fit into such business environment. The proposed schemes are proven to be computationally secure and resist collusion between new joined users and revoked users together with forward and backward secrecy. The security proof is in an appropriate security model. Moreover, our schemes do not forbid revoked users from rejoining in later sessions unlike the existing self- healing key distribution schemes

Online Subset Selection using α\alpha-Core with no Augmented Regret

We consider the problem of sequential sparse subset selections in an online learning setup. Assume that the set $[N]$ consists of $N$ distinct elements. On the $t^{\text{th}}$ round, a monotone reward function $f_t: 2^{[N]} \to \mathbb{R}_+,$ which assigns a non-negative reward to each subset of $[N],$ is revealed to a learner. The learner selects (perhaps randomly) a subset $S_t \subseteq [N]$ of $k$ elements before the reward function $f_t$ for that round is revealed $(k \leq N)$. As a consequence of its choice, the learner receives a reward of $f_t(S_t)$ on the $t^{\text{th}}$ round. The learner's goal is to design an online subset selection policy to maximize its expected cumulative reward accrued over a given time horizon. In this connection, we propose an online learning policy called SCore (Subset Selection with Core) that solves the problem for a large class of reward functions. The proposed SCore policy is based on a new concept of $\alpha$-Core, which is a generalization of the notion of Core from the cooperative game theory literature. We establish a learning guarantee for the SCore policy in terms of a new performance metric called $\alpha$-augmented regret. In this new metric, the power of the offline benchmark is suitably augmented compared to the online policy. We give several illustrative examples to show that a broad class of reward functions, including submodular, can be efficiently learned with the SCore policy. We also outline how the SCore policy can be used under a semi-bandit feedback model and conclude the paper with a number of open problems

Fault analysis and weak key-IV attack on Sprout

Armknecht and Mikhalev proposed a new stream cipher `Sprout\u27 based on the design specification of the stream cipher, Grain-128a. Sprout has shorter state size than Grain family with a round key function. The output of the round key function is XOR\u27ed with the feedback bit of the NFSR of the cipher. In this paper, we propose a new fault attack on Sprout by injecting a single bit fault after the key initialization phase at any arbitrary position of the NFSR of the cipher. By injecting a single bit fault, we recover the bits of the secret key of the cipher by observing the normal and faulty keystream bits at certain clockings of the cipher. By implementing the attack, we verify our result for one particular case. We also show that the Sprout generates same states for several rounds in key initialization phase for two different key-IV pairs, which proves that the key initialization round is having very poor period

A New Cryptanalytic Time/Memory/Data Trade-off Algorithm

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\u27s 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