Department of Computer Science & Engineering

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The problem of target set selection for large scale social networks is addressed in the paper. We describe a novel deprecation based greedy strategy to be applied over a pre-ordered (as obtained with any heuristic influence function) set of nodes. The proposed algorithm runs in iteration and has two stages, (i) Estimation: where the performance of each node is evaluated and (ii) Marking: where the nodes to be deprecated in later iterations are marked. We have theoretically proved that for any monotonic and sub-modular influence function, the algorithm correctly identifies the nodes to be deprecated. For any finite set of input nodes it is shown that the algorithm can meet the ending criteria. The worst case performance of the algorithm, both in terms of time and performance, is also analyzed. Experimental results on seven un-weighted as well as weighted social networks show that the proposed strategy improves the ranking of the input seeds in terms of the total number of nodes influenced. © 2015 Elsevier Inc. All rights reserved.

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Information Sciences

Deprecation based greedy strategy for target set selection in large scale social networks

Graph algorithms play a prominent role in several fields of sciences and engineering. Notable among them are graph traversal, finding the connected components of a graph, and computing shortest paths. There are several efficient implementations of the above problems on a variety of modern multiprocessor architectures. It can be noticed in recent times that the size of the graphs that correspond to real world datasets has been increasing. Parallelism offers only a limited succor to this situation as current parallel architectures have severe short-comings when deployed for most graph algorithms. At the same time, these graphs are also getting very sparse in nature. This calls for particular solution strategies aimed at processing large, sparse graphs on modern parallel architectures. In this paper, we introduce graph pruning as a technique that aims to reduce the size of the graph. Certain elements of the graph can be pruned depending on the nature of the computation. Once a solution is obtained on the pruned graph, the solution is extended to the entire graph. Towards, this end we investigate pruning based on two strategies that justifies their use in current real world graphs. We apply the above technique on three fundamental graph algorithms: breadth first search (BFS), Connected Components (CC), and All Pairs Shortest Paths (APSP). For experimentations, we use three different sources for real world graphs. To validate our technique, we implement our algorithms on a heterogeneous platform consisting of a multicore CPU and a GPU. On this platform, we achieve an average of 35% improvement compared to state-of-the-art solutions. Such an improvement has the potential to speed up other applications reliant on these algorithms. © 2014 Elsevier Inc. All rights reserved.

Journal of Parallel and Distributed Computing

Work efficient parallel algorithms for large graph exploration on emerging heterogeneous architectures

Password Hashing, a technique commonly implemented by a server to protect passwords of clients, by performing a one-way transformation on the password, turning it into another string called the hashed password. In this paper, we introduce a secure password hashing framework Rig which is based on secure cryptographic hash functions. It provides the flexibility to choose different functions for different phases of the construction. The design of the scheme is very simple to implement in software and is flexible as the memory parameter is independent of time parameter (no actual time and memory trade-off) and is strictly sequential (difficult to parallelize) with comparatively huge memory consumption that provides strong resistance against attackers using multiple processing units. It supports client-independent updates, i.e., the server can increase the security parameters by updating the existing password hashes without knowing the password. Rig can also support the server relief protocol where the client bears the maximum effort to compute the password hash, while there is minimal effort at the server side. We analyze Rig and show that our proposal provides an exponential time complexity against the low-memory attack. © Springer International Publishing Switzerland 2015.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Rig: A simple, secure and flexible design for password hashing

The CAESAR competition for standardization of schemes for authenticated encryption has received 49 entries. Constructions such as Keyak, ICEPOLE, Artemia, NORX and Ascon use DuplexWrap and JHAE modes. DuplexWrap is based on the sponge construction and JHAE is based on the JH hash function. Andreeva et al. have recently defined a generalized sponge like construction called Parazoa hash family and provided indifferentiability security bound for the same. They had shown that the sponge as well as the JH hash function are instances of the parazoa construction with suitable choices of parameters. In our work, we define PPAE as an Authenticated Encryption family based on Parazoa construction. The proposed AE mode supports feed-forward operation which is lacking in sponge based AE constructions. We also provide security analysis of the PPAE family. © Springer International Publishing Switzerland 2015.

PPAE: Practical parazoa authenticated encryption family

OAEP and other similar schemes proven secure in Random- Oracle Model require one or more hash functions with output size larger than those of standard hash functions. In this paper, we show that by utilizing popular Sponge constructions in OAEP framework, we can eliminate the need of such hash functions.We provide a new scheme in OAEP framework based on Sponge construction and call our scheme Sponge based asymmetric encryption padding (SpAEP). SpAEP is based on 2 functions: Sponge and Sponge Wrap, and requires only standard output sizes proposed and standardized for Sponge functions. Our scheme is CCA2 secure for any trapdoor one-way permutation in the ideal permutation model for arbitrary length messages. Our scheme utilizes the versatile Sponge function to enhance the capability and efficiency of the OAEP framework. SpAEP with any trapdoor one-way permutation can also be used as a key encapsulation mechanism and a tag-based key encapsulation mechanism for hybrid encryption. Our scheme SpAEP utilizes the permutation model efficiently in the setting of public key encryption in a novel manner. © Springer International Publishing Switzerland 2015.

Sponge based CCA2 secure asymmetric encryption for arbitrary length message

At Asiacrypt 2012, Peyrin et al. showed generic attacks against the HMAC design. They utilized a pair of related keys where only the relation between the keys is known to the attacker but not the keys themselves (the secret key model). On similar lines, at Crypto 2012, Dodis et al. showed differentiability attacks based on ambiguous and colliding keys on HMAC in known/chosen key model. Peyrin et al. also proposed a patching scheme for HMAC and claimed that the proposed patch thwarts their attacks. In this work, we first show that the patch proposed by Peyrin et al. will not prevent their attacks for the HMAC construction for certain “good” cryptographic hash functions. Specifically, we show that no public and reversible patch will prevent their attack on HMAC instantiated with a weakly collision resistant hash function. Following this, we propose two different patches, called the secret patch and the collision resistant one way (CrOw) patch, to thwart the attacks of Peyrin et al. and Dodis et al. Our work is theoretical in nature, and does not threaten the security of HMAC used with standard hash functions. Further, both our patches are designed to be used as wrappers and do not affect the underlying HMAC construction. This property is similar to Peyrin et al.’s patch. © Springer International Publishing Switzerland 2015.

New HMAC message patches: Secret patch and CrOw patch

The ARIA block cipher has been established as a Korean encryption standard by Korean government since 2004. In this work, we re-evaluate the security bound of reduced round ARIA-192 and ARIA- 256 against meet-in-the-middle (MITM) key recovery attacks in the single key model. We present a new 4-round distinguisher to demonstrate the best 7 &amp; 8 round MITM attacks on ARIA-192/256. Our 7-round attack on ARIA-192 has data, time and memory complexity of 2113, 2135.1 and 2130 respectively. For our 7-round attack on ARIA-256, the data/time/memory complexities are 2115, 2136.1 and 2130 respectively. These attacks improve upon the previous best MITM attack on the same in all the three dimensions. Our 8-round attack on ARIA-256 requires 2113 cipher calls and has time and memory complexity of 2245.9 and 2138 respectively. This improves upon the previous best MITM attack on ARIA-256 in terms of time as well as memory complexity. Further, in our attacks, we are able to recover the actual secret key unlike the previous cryptanalytic attacks existing on ARIA-192/256. To the best of our knowledge, this is the first actual key recovery attack on ARIA so far. We apply multiset attack - a variant of meet-in-the-middle attack to achieve these results. © Springer International Publishing Switzerland 2015.

Improved meet-in-the-middle attacks on 7 and 8-round ARIA-192 and ARIA-256

Guided by a polymath approach—encompassing neuroscience, philosophy, psychology and computer science, this article describes a novel ‘cognitive’ computational mind framework for text comprehension in terms of Minsky’s ‘Society of Mind’ and ‘Emotion Machine’ theories. Observing a top-down design method, we enumerate here the macrocosmic elements of the model—the ‘agencies’ and memory constructs, followed by an elucidation on the working principles and synthesis concerns. Besides corroboration of results of a dry-run test by thoughts generated by random human subjects; the completeness of the conceptualized framework has been validated as a consequence of its total representation of ‘text understanding’ functions of the human brain, types of human memory and emulation of the layers of the mind. A brief conceptual comparison, between the architecture and existing ‘conscious’ agents, has been included as well. The framework, though observed here in its capacity as a text comprehender, is capable of understanding in general. A cognitive model of text comprehension, besides contributing to the ‘thinking machines’ research enterprise, is envisioned to be strategic in the design of intelligent plagiarism checkers, literature genre-cataloguers, differential diagnosis systems, and educational aids for children with reading disorders. Turing’s landmark 1950 article on computational intelligence is the principal motivator behind our research initiative. © 2015, Springer Science+Business Media Dordrecht.

Natural Computing

Text comprehension and the computational mind-agencies

In this paper we propose a framework for recognition and retrieval tasks in the context of scene text images. In contrast to many of the recent works, we focus on the case where an image-specific list of words, known as the small lexicon setting, is unavailable. We present a conditional random field model defined on potential character locations and the interactions between them. Observing that the interaction potentials computed in the large lexicon setting are less effective than in the case of a small lexicon, we propose an iterative method, which alternates between finding the most likely solution and refining the interaction potentials. We evaluate our method on public datasets and show that it improves over baseline and state-of-the-art approaches. For example, we obtain nearly 15% improvement in recognition accuracy and precision for our retrieval task over baseline methods on the IIIT-5K word dataset, with a large lexicon containing 0.5 million words. © Springer International Publishing Switzerland 2015.

Scene text recognition and retrieval for large lexicons

The paper tackles the task of preserving depth cue in single images for human perception of depth despite compression artifact. The process is mainly divided into 2 steps; saliency map computation and image compression on the basis of salient regions. The idea is that the regions which are less blurred should be more salient rather than blurred regions. For saliency computation the proposed method first obtains the blur information for the image using frequency informa- tion. Then we generate saliency map by region combining on the basis of this blur index. These saliency values are used to vary the quality parameter over the blocks in the image for JPEG compression. Experimental results show that the proposed scheme yields good results over normal JPEG compresssion in terms of PSNR values. Copyright 2015 ACM.