Stash in a Flash

Encryption is a useful tool to protect data confidentiality. Yet it is still challenging to hide the very presence of encrypted, secret data from a powerful adversary. This paper presents a new technique to hide data in flash by manipulating the voltage level of pseudo-randomlyselected flash cells to encode two bits (rather than one) in the cell. In this model, we have one “public” bit interpreted using an SLC-style encoding, and extract a private bit using an MLC-style encoding. The locations of cells that encode hidden data is based on a secret key known only to the hiding user. Intuitively, this technique requires that the voltage level in a cell encoding data must be (1) not statistically distinguishable from a cell only storing public data, and (2) the user must be able to reliably read the hidden data from this cell. Our key insight is that there is a wide enough variation in the range of voltage levels in a typical flash device to obscure the presence of fine-grained changes to a small fraction of the cells, and that the variation is wide enough to support reliably re-reading hidden data. We demonstrate that our hidden data and underlying voltage manipulations go undetected by support vector machine based supervised learning which performs similarly to a random guess. The error rates of our scheme are low enough that the data is recoverable months after being stored. Compared to prior work, our technique provides 24x and 50x higher encoding and decoding throughput and doubles the capacity, while being 37x more power efficient

System noise, OS clock ticks, and fine-grained parallel applications

As parallel jobs get bigger in size and finer in granularity, “system noise ” is increasingly becoming a problem. In fact, fine-grained jobs on clusters with thousands of SMP nodes run faster if a processor is intentionally left idle (per node), thus enabling a separation of “system noise ” from the com-putation. Paying a cost in average processing speed at a node for the sake of eliminating occasional processes delays is (unfortunately) beneficial, as such delays are enormously magnified when one late process holds up thousands of peers with which it synchronizes. We provide a probabilistic argument showing that, under certain conditions, the effect of such noise is linearly pro-portional to the size of the cluster (as is often empirically observed). We then identify a major source of noise to be indirect overhead of periodic OS clock interrupts (“ticks”), that are used by all general-purpose OSs as a means of main-taining control. This is shown for various grain sizes, plat-forms, tick frequencies, and OSs. To eliminate such noise, we suggest replacing ticks with an alternative mechanism we call “smart timers”. This turns out to also be in line with needs of desktop and mobile computing, increasing the chances of the suggested change to be accepted. 1

Stash in a Flash

Encryption is a useful tool to protect data confidentiality. Yet it is still challenging to hide the very presence of encrypted, secret data from a powerful adversary. This paper presents a new technique to hide data in flash by manipulating the voltage level of pseudo-randomlyselected flash cells to encode two bits (rather than one) in the cell. In this model, we have one “public” bit interpreted using an SLC-style encoding, and extract a private bit using an MLC-style encoding. The locations of cells that encode hidden data is based on a secret key known only to the hiding user. Intuitively, this technique requires that the voltage level in a cell encoding data must be (1) not statistically distinguishable from a cell only storing public data, and (2) the user must be able to reliably read the hidden data from this cell. Our key insight is that there is a wide enough variation in the range of voltage levels in a typical flash device to obscure the presence of fine-grained changes to a small fraction of the cells, and that the variation is wide enough to support reliably re-reading hidden data. We demonstrate that our hidden data and underlying voltage manipulations go undetected by support vector machine based supervised learning which performs similarly to a random guess. The error rates of our scheme are low enough that the data is recoverable months after being stored. Compared to prior work, our technique provides 24x and 50x higher encoding and decoding throughput and doubles the capacity, while being 37x more power efficient

A Survey for Large Separation Lensed FIRST Quasars

Little is known about the statistics of gravitationally lensed quasars at large (7''-30'') image separations, which probe masses on the scale of galaxy clusters. We have carried out a survey for gravitationally-lensed objects, among sources in the FIRST 20cm radio survey that have unresolved optical counterparts in the digitizations of the Palomar Observatory Sky Survey. From the statistics of ongoing surveys that search for quasars among FIRST sources, we estimate that there are about 9100 quasars in this source sample, making this one of the largest lensing surveys to date. Using broad-band imaging, we have isolated all objects with double radio components separated by 5''-30'', that have unresolved optical counterparts with similar BVI colours. Our criteria for similar colours conservatively allow for observational error and for colour variations due to time delays between lensed images. Spectroscopy of these candidates shows that none of the pairs are lensed quasars. This sets an upper limit (95% confidence) on the lensing fraction in this survey of 3.3x10^-4, assuming 9100 quasars. Although the source redshift distribution is poorly known, a rough calculation of the expected lensing frequency and the detection efficiencies and biases suggests that simple theoretical expectations are of the same order of magnitude as our observational upper limit. Our procedure is novel in that our exhaustive search for lensed objects does not require prior identification of the quasars in the sample as such. Characterization of the FIRST-selected quasar population will enable using our result to constrain quantitatively the mass properties of clusters.Comment: 10 pages, accepted for publication in MNRA

Instability in parallel job scheduling simulation: the role of workload flurries

The performance of computer systems depends, among other things, on the workload. This motivates the use of real workloads (as recorded in activity logs) to drive simulations of new designs. Unfortunately, real workloads may contain various anomalies that contaminate the data. A previously unrecognized type of anomaly is workload flurries: rare surges of activity with a repetitive nature, caused by a single user, that dominate the workload for a relatively short period. We find that long workloads often include at least one such event. We show that in the context of parallel job scheduling these events can have a significant effect on performance evaluation results, e.g. a very small perturbation of the simulation conditions might lead to a large and disproportional change in the outcome. This instability is due to jobs in the flurry being effected in unison, a consequence of the flurry’s repetitive nature. We therefore advocate that flurries be filtered out before the workload is used, in order to achieve stable and more reliable evaluation results (analogously to the removal of outliers in statistical analysis). At the same time, we note that more research is needed on the possible effects of flurries.