Inducing data loss in Zigbee networks via join/association handshake spoofing

Abstract This paper discusses the physics, definitions, and nanoprobing flow of a flash bit memory. In addition, a case study showing the effectiveness of nanoprobing in detecting the Single Bit Fail Data Gain and Data Loss in Flash Memory is also discussed. The paper also includes cases where no passive voltage contrast was observed at the SEM and no leakage was observed at AFM, yet the units failing SBF DG, SBF DL and depletion, were detected by nanoprobing of the single bit. The major finding of this paper is a way to resolve data gain, data loss, and depletion failures of flash memory by nanoprobing procedure, despite no PVC seen at the SEM and no leakage seen at the AFM.

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Liability for Data Loss

SSRN Electronic Journal ◽

10.2139/ssrn.3237407 ◽

2018 ◽

Cited By ~ 1

Author(s):

Vincenzo Zeno-Zencovich

Keyword(s):

Data Loss

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Increased yields of duplex sequencing data by a series of quality control tools

NAR Genomics and Bioinformatics ◽

10.1093/nargab/lqab002 ◽

2021 ◽

Vol 3 (1) ◽

Author(s):

Gundula Povysil ◽

Monika Heinzl ◽

Renato Salazar ◽

Nicholas Stoler ◽

Anton Nekrutenko ◽

...

Keyword(s):

Low Frequency ◽

Variant Calling ◽

Data Loss ◽

Sequencing Data ◽

Bioinformatics Pipeline ◽

Consensus Sequences ◽

Sequencing Errors ◽

Data Output ◽

Reverse Strand ◽

Duplex Sequencing

Abstract Duplex sequencing is currently the most reliable method to identify ultra-low frequency DNA variants by grouping sequence reads derived from the same DNA molecule into families with information on the forward and reverse strand. However, only a small proportion of reads are assembled into duplex consensus sequences (DCS), and reads with potentially valuable information are discarded at different steps of the bioinformatics pipeline, especially reads without a family. We developed a bioinformatics toolset that analyses the tag and family composition with the purpose to understand data loss and implement modifications to maximize the data output for the variant calling. Specifically, our tools show that tags contain polymerase chain reaction and sequencing errors that contribute to data loss and lower DCS yields. Our tools also identified chimeras, which likely reflect barcode collisions. Finally, we also developed a tool that re-examines variant calls from raw reads and provides different summary data that categorizes the confidence level of a variant call by a tier-based system. With this tool, we can include reads without a family and check the reliability of the call, that increases substantially the sequencing depth for variant calling, a particular important advantage for low-input samples or low-coverage regions.

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Robust Secret Image Sharing Resistant to Noise in Shares

ACM Transactions on Multimedia Computing Communications and Applications ◽

10.1145/3419750 ◽

2021 ◽

Vol 17 (1) ◽

pp. 1-22

Author(s):

Xuehu Yan ◽

Lintao Liu ◽

Longlong Li ◽

Yuliang Lu

Keyword(s):

Chinese Remainder Theorem ◽

Recovery Phase ◽

Error Correcting Codes ◽

Secret Image Sharing ◽

Data Loss ◽

Least Significant Bit ◽

Recovery Method ◽

Secret Image ◽

Image Sharing ◽

Salt And Pepper

A secret image is split into shares in the generation phase of secret image sharing (SIS) for a threshold. In the recovery phase, the secret image is recovered when any or more shares are collected, and each collected share is generally assumed to be lossless in conventional SIS during storage and transmission. However, noise will arise during real-world storage and transmission; thus, shares will experience data loss, which will also lead to data loss in the secret image being recovered. Secret image recovery in the case of lossy shares is an important issue that must be addressed in practice, which is the overall subject of this article. An SIS scheme that can recover the secret image from lossy shares is proposed in this article. First, robust SIS and its definition are introduced. Next, a robust SIS scheme for a threshold without pixel expansion is proposed based on the Chinese remainder theorem (CRT) and error-correcting codes (ECC). By screening the random numbers, the share generation phase of the proposed robust SIS is designed to implement the error correction capability without increasing the share size. Particularly in the case of collecting noisy shares, our recovery method is to some degree robust to some noise types, such as least significant bit (LSB) noise, JPEG compression, and salt-and-pepper noise. A theoretical proof is presented, and experimental results are examined to evaluate the effectiveness of our proposed method.

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