Cross-view Geo-localization Based on Cross-domain Matching
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Keywords:
Discriminative model
Feature (linguistics)
Shuffling
Margin (machine learning)
This paper shows an example application system that supports the users to find a suitable customized shuffling method for playing card games. To enable the users to utilize the characteristics of each shuffling method in each game playing, we develop a prototype gaming platform with a shuffling adjustment support mechanism that assists players in selecting the preferred shuffling method and the custom number of shuffling times to fit to their playing styles. We also develop a prototype player agent that behaves as the good counterpart in the exciting gameplay defined by Isabella et al. in 2004, applying QS-learning technique to allow the agent to change its behavior according to the characteristics of the shuffling.
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Motivated by recent developments in the shuffle model of differential privacy, we propose a new approximate shuffling functionality called Alternating Shuffle, and provide a protocol implementing alternating shuffling in a single-server threat model where the adversary observes all communication. Unlike previous shuffling protocols in this threat model, the per-client communication of our protocol only grows sub-linearly in the number of clients. Moreover, we study the concrete efficiency of our protocol and show it can improve per-client communication by one or more orders of magnitude with respect to previous (approximate) shuffling protocols. We also show a differential privacy amplification result for alternating shuffling analogous to the one for uniform shuffling, and demonstrate that shuffling-based protocols for secure summation based a construction of Ishai et al. (FOCS'06) remain secure under the Alternating Shuffle. In the process we also develop a protocol for exact shuffling in single-server threat model with amortized logarithmic communication per-client which might be of independent interest.
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Even though cryptographic algorithms embedded on physical devices guarantee theoretical security, they are vulnerable to side channel attacks that analyze correlations related to physical information such as power consumption and electromagnetic waves. Physical devices without any countermeasures are vulnerable to side channel analysis. The masking and shuffling techniques the most used countermeasures against side channel analysis. Masking techniques rely on the masking order, however, these techniques have a high computational cost. Shuffling techniques, on the other hand, are able to provide security without high computational cost. Recently, instead of using one countermeasure alone, a combination of them has been employed while still affording provable security at a relatively computational cost. Computational security is related to the complexity of shuffling when a shuffling technique has been employed. In this paper, we apply shuffling techniques of the Advanced Encryption Standard (AES) in a new way. Our technique involves to eight different implementations of AES. If our technique is proven safety, then we will combine masking techniques and our technique. So, we examine the theoretical versus experimentally analyzed number of power traces for the recovery key. Theoretically, our results show 64 times more shuffling complexity than a non-shuffling AES implementation. Experimentally, however, it has seven times greater shuffling complexity. Keywords: Countermeasure, Shuffling Technique, Side Channel Analysis
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We address the issue of shuffling loads in automated storage/retrieval system (AS/RS). To minimize the response time of retrievals, we pre-sort the loads into any specified locations. 1D, 2D and 3D AS/RS racks have been designed to achieve the shuffling efficiently. The corresponding shuffling algorithms are described in detail. The response time of retrieval, the lower and upper bounds of energy consumption are also derived. Results of the analysis and numerical experiments show that the shuffling algorithms are quite efficient indeed.
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DNA shuffling
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We consider a problem of shuffling a deck of cards with ordered labels. Namely we split the deck of N=k^tq cards (where t>=1 is maximal) into k equally sized stacks and then take the top card off of each stack and sort them by the order of their labels and add them to the shuffled stack. We show how to find stacks of cards invariant and periodic under the shuffling. We also show when gcd(q,k)=1 the possible periods of this shuffling are all divisors of order_k(N-q).
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DNA shuffling
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Shuffling
Soundness
Zero-knowledge proof
DNA shuffling
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Shuffling
DNA shuffling
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We consider a problem of shuffling a deck of cards with ordered labels. Namely we split the deck of N=k^tq cards (where t>=1 is maximal) into k equally sized stacks and then take the top card off of each stack and sort them by the order of their labels and add them to the shuffled stack. We show how to find stacks of cards invariant and periodic under the shuffling. We also show when gcd(q,k)=1 the possible periods of this shuffling are all divisors of order_k(N-q).
Shuffling
DNA shuffling
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Motivated by recent developments in the shuffle model of differential privacy, we propose a new approximate shuffling functionality called Alternating Shuffle, and provide a protocol implementing alternating shuffling in a single-server threat model where the adversary observes all communication. Unlike previous shuffling protocols in this threat model, the per-client communication of our protocol only grows sub-linearly in the number of clients. Moreover, we study the concrete efficiency of our protocol and show it can improve per-client communication by one or more orders of magnitude with respect to previous (approximate) shuffling protocols. We also show a differential privacy amplification result for alternating shuffling analogous to the one for uniform shuffling, and demonstrate that shuffling-based protocols for secure summation based a construction of Ishai et al. remain secure under the Alternating Shuffle. In the process we also develop a protocol for exact shuffling in single-server threat model with amortized logarithmic communication per-client which might be of independent interest.
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We revisit the popular adage that side-channel countermeasures must be combined to be efficient, and study its application to bitslice masking and shuffling. Our main contributions are twofold. First, we improve this combination: by shuffling the shares of a masked implementation rather than its tuples, we can amplify the impact of the shuffling exponentially in the number of shares, while this impact was independent of the masking security order in previous works. Second, we evaluate the masking and shuffling combination’s performance vs. security tradeoff under sufficient noise conditions: we show that the best approach is to mask first (i.e., fill the registers with as many shares as possible) and shuffle the independent operations that remain. We conclude that with moderate but sufficient noise, the “bitslice masking + shuffling” combination of countermeasures is practically relevant, and its interest increases when randomness is expensive and many independent operations are available for shuffling. When these conditions are not met, masking only is the best option. As additional side results, we improve the best known attack against the shuffling countermeasure from ASIACRYPT 2012. We also recall that algorithmic countermeasures like masking and shuffling, and therefore their combination, cannot be implemented securely without a minimum level of physical noise.
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