Secure secret sharing in the cloud

In this paper, we show how a dealer with limited resources is possible to share the secrets to players via an untrusted cloud server without compromising the privacy of the secrets. This scheme permits a batch of two secret messages to be shared to two players in such a way that the secrets are reconstructable if and only if two of them collaborate. An individual share reveals absolutely no information about the secrets to the player. The secret messages are obfuscated by encryption and thus give no information to the cloud server. Furthermore, the scheme is compatible with the Paillier cryptosystem and other cryptosystems of the same type. In light of the recent developments in privacy-preserving watermarking technology, we further model the proposed scheme as a variant of reversible watermarking in the encrypted domain

Reversible data hiding in JPEG images based on adjustable padding

In this paper, we propose a reversible data hiding scheme that enables an adjustable amount of information to be embedded in JPEG images based on padding strategy. The proposed embedding algorithm only modifies, in a subtle manner, an adjustable number of zero-valued quantised DCT coefficients to embed the message. Hence, compared with a state-of-the-art based on histogram shifting, the proposed scheme has a relatively low distortion to the host images. In addition to this, we found that by representing the message in ternary instead of in binary, we can embed a greater amount of information while the level of distortion remains unchanged. Experimental results support that the proposed scheme can achieve better visual quality of the marked JPEG image than the histogram shifting based scheme. The proposed scheme also outperforms this state-of-the-art in terms of the ease of implementation

GreedyDual-Join: Locality-Aware Buffer Management for Approximate Join Processing Over Data Streams

We investigate adaptive buffer management techniques for approximate evaluation of sliding window joins over multiple data streams. In many applications, data stream processing systems have limited memory or have to deal with very high speed data streams. In both cases, computing the exact results of joins between these streams may not be feasible, mainly because the buffers used to compute the joins contain much smaller number of tuples than the tuples contained in the sliding windows. Therefore, a stream buffer management policy is needed in that case. We show that the buffer replacement policy is an important determinant of the quality of the produced results. To that end, we propose GreedyDual-Join (GDJ) an adaptive and locality-aware buffering technique for managing these buffers. GDJ exploits the temporal correlations (at both long and short time scales), which we found to be prevalent in many real data streams. We note that our algorithm is readily applicable to multiple data streams and multiple joins and requires almost no additional system resources. We report results of an experimental study using both synthetic and real-world data sets. Our results demonstrate the superiority and flexibility of our approach when contrasted to other recently proposed techniques

PowerPack: Energy Profiling and Analysis of High-Performance Systems and Applications

Energy efficiency is a major concern in modern high-performance computing system design. In the past few years, there has been mounting evidence that power usage limits system scale and computing density, and thus, ultimately system performance. However, despite the impact of power and energy on the computer systems community, few studies provide insight to where and how power is consumed on high-performance systems and applications. In previous work, we designed a framework called PowerPack that was the first tool to isolate the power consumption of devices including disks, memory, NICs, and processors in a high-performance cluster and correlate these measurements to application functions. In this work, we extend our framework to support systems with multicore, multiprocessor-based nodes, and then provide in-depth analyses of the energy consumption of parallel applications on clusters of these systems. These analyses include the impacts of chip multiprocessing on power and energy efficiency, and its interaction with application executions. In addition, we use PowerPack to study the power dynamics and energy efficiencies of dynamic voltage and frequency scaling (DVFS) techniques on clusters. Our experiments reveal conclusively how intelligent DVFS scheduling can enhance system energy efficiency while maintaining performance