A Practical Searchable Symmetric Encryption Scheme for Smart Grid Data

Outsourcing data storage to the remote cloud can be an economical solution to enhance data management in the smart grid ecosystem. To protect the privacy of data, the utility company may choose to encrypt the data before uploading them to the cloud. However, while encryption provides confidentiality to data, it also sacrifices the data owners' ability to query a special segment in their data. Searchable symmetric encryption is a technology that enables users to store documents in ciphertext form while keeping the functionality to search keywords in the documents. However, most state-of-the-art SSE algorithms are only focusing on general document storage, which may become unsuitable for smart grid applications. In this paper, we propose a simple, practical SSE scheme that aims to protect the privacy of data generated in the smart grid. Our scheme achieves high space complexity with small information disclosure that was acceptable for practical smart grid application. We also implement a prototype over the statistical data of advanced meter infrastructure to show the effectiveness of our approach

Lying Aversion and Vague Communication: An Experimental Study

An agent may strategically employ a vague message to mislead an audience's belief about the state of the world, but this may cause the agent to feel guilt or negatively impact how the audience perceives the agent. Using a novel experimental design that allows participants to be vague while at the same time isolating the internal cost of lying from the social identity cost of appearing dishonest, we explore the extent to which these two types of lying costs affect communication. We find that participants exploit vagueness to be consistent with the truth, while at the same time leveraging the imprecision to their own benefit. More participants use vague messages in treatments where concern with social identity is relevant. In addition, we find that social identity concerns substantially affect the length and patterns of vague messages used across the treatments

Using Adaptive Psychophysics to Identify the Neural Network Reset Time in Subsecond Interval Timing

State dependent network models of subsecond interval timing propose that duration is encoded in states of neuronal populations that need to reset prior to a novel timing operation in order to maintain optimal timing performance. Previous research has shown that the approximate boundary of this reset interval can be inferred by varying the interstimulus interval between two to-be-timed intervals. However, the estimated boundary of this reset interval is broad (250-500ms) and remains underspecified with implications for the characteristics of state dependent network dynamics subserving interval timing. Here we probed the interval specificity of this reset boundary by manipulating the interstimulus interval between standard and comparison intervals in two subsecond auditory duration discrimination tasks (100 and 200ms) and a control (pitch) discrimination task using adaptive psychophysics. We found that discrimination thresholds improved with the introduction of a 333ms interstimulus interval relative to a 250ms interstimulus interval in both duration discrimination tasks, but not in the control task. This effect corroborates previous findings of a breakpoint in the discrimination performance for subsecond stimulus interval pairs as a function of an incremental interstimulus delay but more precisely localizes the minimal interstimulus delay range. These results suggest that state dependent networks subserving subsecond timing require approximately 250-333ms for the network to reset in order to maintain optimal interval timing

How good are Global Newton methods? Part 2

Newton's method applied to certain problems with a discontinuous derivative operator is shown to be effective. A global Newton method in this setting is exhibited and its computational complexity is estimated. As an application a method is proposed to solve problems of linear inequalities (linear programming, phase 1). Using an example of the Klee-Minty type due to Blair, it was found that the simplex method (used in super-lindo) required over 2,000 iterations, while the method above required an average of 8 iterations (Newton steps) over 15 random starting values.Naval Surface Weapons Center, Dahlgren, VAhttp://archive.org/details/howgoodareglobal00goldO&MN Direct FundingApproved for public release; distribution is unlimited