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Incorporating Randomness into DNA Steganography to Realize Secondary Secret key, Self-destruction, and Quantum Key Distribution-like Function

Zhang, Y.; Cui, M.

2019-08-05 synthetic biology
10.1101/725499 bioRxiv
Show abstract

DNA has become a promising candidate as future data storage medium, which makes DNA steganography indispensable in DNA data security. While PCR primers are conventional secret keys in DNA steganography, the information can be read once the primers are intercepted. New steganography approach is needed to make the DNA-encoded information safer, if not unhackable. Herein, by mixing information-carrying DNA with partially degenerated DNA library containing single or multiple restriction sites, we build an additional protective layer, which can be removed by desired restriction enzymes as secondary secret keys. As PCR is inevitable for reading DNA-encrypted information, heating will cause reshuffling and generate endonuclease-resistant mismatched duplexes, especially for DNA with high sequence diversity. Consequently, with the incorporation of randomness, the DNA steganography possesses both quantum key distribution (QKD)-like function for detecting PCR by an interceptor and self-destructive property. With a DNA-ink incorporating the steganography, the authenticity of a writing can be confirmed only by authorized person with the knowledge of all embedded keys.

Matching journals

The top 9 journals account for 50% of the predicted probability mass.

1
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Top 15%
11.9%
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2
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7
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Top 8%
4.0%
8
Angewandte Chemie International Edition
○ 93 papers in training set
Top 0.4%
3.5%
9
Journal of the American Chemical Society
● 217 papers in training set
Top 1%
3.2%
50% of probability mass above
10
Nucleic Acids Research
◐ 1281 papers in training set
Top 7%
2.4%
11
National Science Review
◐ 21 papers in training set
Top 0.1%
2.4%
12
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○ 11 papers in training set
Top 0.1%
2.4%
13
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○ 993 papers in training set
Top 10%
2.1%
14
ACS Nano
● 113 papers in training set
Top 1%
1.9%
15
eLife
● 5828 papers in training set
Top 50%
1.7%
16
Cell
○ 431 papers in training set
Top 6%
1.7%
17
Science China Life Sciences
○ 29 papers in training set
Top 0.2%
1.7%
18
International Journal of Biological Macromolecules
○ 76 papers in training set
Top 0.9%
1.7%
19
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Top 0.8%
1.1%
20
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○ 11 papers in training set
Top 0.2%
1.1%
21
Communications Physics
○ 14 papers in training set
Top 0.1%
1.1%
22
PLOS ONE
● 5266 papers in training set
Top 56%
1.0%
23
Nano Letters
● 71 papers in training set
Top 1%
0.9%
24
Small Methods
○ 29 papers in training set
Top 0.7%
0.8%
25
Life
○ 29 papers in training set
Top 0.9%
0.8%
26
Advanced Materials
○ 56 papers in training set
Top 1%
0.8%
27
Analytical Chemistry
● 218 papers in training set
Top 2%
0.8%
28
The CRISPR Journal
○ 39 papers in training set
Top 0.4%
0.8%
29
Advanced Functional Materials
○ 46 papers in training set
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30
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"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.