Protecting Private Data by Honey Encryption

Yin, Wei; Indulska, Jadwiga; Zhou, Hongjian

doi:10.1155/2017/6760532

Cited by 22 publications

(14 citation statements)

References 7 publications

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“…Figure 4d shows U chem,EAM Nb in NbTaV as an example of the magnitude and localization to atoms in the dislocation core. Table 2: Solute misfit volumes and elastic constants for the alloys studied, as computed using Density Functional Theory (data kindly provided by Dr. B. Yin [39], using methods detailed in Ref. [40]), Vegard's law (misfits) or rule-of-mixtures ROM (elastic constants), the true random alloy described by EAM potentials, and the average-alloy EAM potential.…”

Section: Theory Vs Experimentsmentioning

confidence: 99%

Mechanistic origin of high strength in refractory BCC high entropy alloys up to 1900K

Maresca¹,

Curtin²

2020

Acta Materialia

307

113

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The body centered cubic (BCC) high entropy alloys MoNbTaW and MoNbTaVW show exceptional strength retention up to 1900K. The mechanistic origin of the retained strength is unknown yet is crucial for finding the best alloys across the immense space of BCC HEA compositions. Experiments on Nb-Mo, Fe-Si and Ti-Zr-Nb alloys report decreased mobility of edge dislocations, motivating a theory of strengthening of edge dislocations in BCC alloys. Unlike pure BCC metals and dilute alloys that are controlled by screw dislocation motion at low temperatures, the strength of BCC HEAs can be controlled by edge dislocations, and especially at high temperatures, due to the barriers created for edge glide through the random field of solutes. A parameter-free theory for edge motion in BCC alloys qualitatively and quantitatively captures the strength versus temperature for the MoNbTaW and MoNbTaVW alloys. A reduced analytic version of the theory then enables screening over >600,000 compositions in the Mo-Nb-Ta-V-W family, identifying promising new compositions with high retained strength and/or reduced mass density. Overall, the theory reveals an unexpected mechanism responsible for high temperature strength in BCC alloys and paves the way for theory-guided design of stronger high entropy alloys.

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Section: Theory Vs Experimentsmentioning

confidence: 99%

Mechanistic origin of high strength in refractory BCC high entropy alloys up to 1900K

Maresca¹,

Curtin²

2020

Acta Materialia

307

113

View full text Add to dashboard Cite

show abstract

“…However, the available encryption algorithms are considered to be secure. But depending on the time and computing power, they are also susceptible to intrusions [30]. The present encryption techniques are also beset with vulnerabilities, for instance, when decrypting with a wrongly guessed key, they yield an invalid looking plaintext message, while decrypting with the right key, they give a valid-looking plaintext message, confirming that the ciphertext message is correctly decrypted [30].…”

Section: Materials and Methods Data Encryption Based On Attribute Basedmentioning

confidence: 97%

Peer Review #2 of "Attribute based honey encryption algorithm for securing big data: Hadoop distributed file system perspective (v0.1)"

2020

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Hadoop has become a promising platform to reliably process and store big data. It provides flexible and low cost services to huge data through Hadoop Distributed File System (HDFS) storage. Unfortunately, absence of any inherent security mechanism in Hadoop increases the possibility of malicious attacks on the data processed or stored through Hadoop. In this scenario, securing the data stored in HDFS becomes a challenging task. Hence, researchers and practitioners have intensified their efforts in working on mechanisms that would protect user's information collated in HDFS. This has led to the development of numerous encryption-decryption algorithms but their performance decreases as the file size increases. In the present study, the authors have enlisted a methodology to solve the issue of data security in Hadoop storage. The authors have integrated Attribute Based Encryption with the honey encryption on Hadoop, i.e., Attribute Based Honey Encryption (ABHE). This approach works on files that are encoded inside the HDFS and decoded inside the Mapper. In addition, the authors have evaluated the proposed ABHE algorithm by performing encryption-decryption on different sizes of files and have compared the same with existing ones including AES and AES with OTP algorithms. The ABHE algorithm shows considerable improvement in performance during the encryption-decryption of files.

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“…However, the available encryption algorithms are considered to be secure. But depending on the time and computing power, they are also susceptible to intrusions (Yin, lska & Zhou, 2017). The present encryption techniques are also beset with vulnerabilities, for instance, when decrypting with a wrongly guessed key, they yield an invalid looking plaintext message, while decrypting with the right key, they give a valid-looking plaintext message, confirming that the cipher-text message is correctly decrypted (Yin, lska & Zhou, 2017).…”

Section: Data Encryption Based On Attribute Based Honey Encryption (Amentioning

confidence: 96%

Attribute based honey encryption algorithm for securing big data: Hadoop distributed file system perspective

Kapil

Agrawal

Attaallah

et al. 2020

PeerJ Computer Science

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Hadoop has become a promising platform to reliably process and store big data. It provides flexible and low cost services to huge data through Hadoop Distributed File System (HDFS) storage. Unfortunately, absence of any inherent security mechanism in Hadoop increases the possibility of malicious attacks on the data processed or stored through Hadoop. In this scenario, securing the data stored in HDFS becomes a challenging task. Hence, researchers and practitioners have intensified their efforts in working on mechanisms that would protect user’s information collated in HDFS. This has led to the development of numerous encryption-decryption algorithms but their performance decreases as the file size increases. In the present study, the authors have enlisted a methodology to solve the issue of data security in Hadoop storage. The authors have integrated Attribute Based Encryption with the honey encryption on Hadoop, i.e., Attribute Based Honey Encryption (ABHE). This approach works on files that are encoded inside the HDFS and decoded inside the Mapper. In addition, the authors have evaluated the proposed ABHE algorithm by performing encryption-decryption on different sizes of files and have compared the same with existing ones including AES and AES with OTP algorithms. The ABHE algorithm shows considerable improvement in performance during the encryption-decryption of files.

show abstract

Protecting Private Data by Honey Encryption

Cited by 22 publications

References 7 publications

Mechanistic origin of high strength in refractory BCC high entropy alloys up to 1900K

Mechanistic origin of high strength in refractory BCC high entropy alloys up to 1900K

Peer Review #2 of "Attribute based honey encryption algorithm for securing big data: Hadoop distributed file system perspective (v0.1)"

Attribute based honey encryption algorithm for securing big data: Hadoop distributed file system perspective

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