The advancement in data transfer and storage technology has prompted new challenges for its
secure transmission. Everyday a huge amount of data (images, passwords, bank details etc.)
is transmitted through open channels, making it vulnerable to intruders. To ensure the safe
transmission, several optical and digital encryption techniques have been explored. Mostly,
the security keys used in the existing image encryption methods are computer generated noise
like distributions having uniformly distributed histogram. Statistically, an attacker may
retrieve these keys if he/she will have partial/full knowledge of the cryptosystem or its
constituents. In this paper, we propose a new asymmetric cryptosystem for phase image
encryption which uses the physically unclonable functions (PUFs) as security keys. The
PUFs are the speckle patterns recorded optically by scattering of vortex beams through
ground glass diffuser (GGD) having unique properties. For encryption, the original amplitude
image is first converted to a phase image and modulated with a PUF to get the complex
image. This complex image is then illuminated with a plane wave and the complex wavefront
at a distance d is recorded. The real part of the complex wavefront is further processed to get
the encrypted image and the imaginary part is kept as the first private key. Polar
decomposition approach is utilized for generating two more private security keys and to
enable the multi-user capability in the cryptosystem. Numerical simulation results consisting
of encryption/decryption, key sensitivity and robustness analysis will be shown in support of
the proposed method.
The advancement in data transfer and storage technology has prompted new challenges for its
secure transmission.
The advancement in data transfer and storage technology has prompted new challenges for its
secure transmission.
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On the other hand, these keys might not be very complicated or variable in the long run, which could leave them open to advanced cryptanalysis attacks.
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[This link](http://example.net)
[KEY]: http://example.com/
example
Key: http://example.com/
[url=https://example.com/]example[/url]
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What stands out is that beyond the mathematical optimization, the outcomes are also highly dependent on how participants interpret incentives and respond to uncertainty within the system. For example, the trade-off between pricing and relationship resilience highlights that even rational models are sensitive to behavioral responses and perception of value. ([MDPI][1])
In that sense, there is an interesting connection between formal decision models and human-centered interpretation. Even outside economic or platform environments, similar challenges arise when individuals need to process incomplete information and balance competing factors. Some perspectives approach this through the use of **[decision reflection frameworks](https://www.ichizenn.com/koi-mikuji/)** that help structure thinking and reduce ambiguity when evaluating uncertain outcomes.
Bridging quantitative optimization models with a deeper understanding of how decisions are actually perceived and processed by users could be a valuable direction for future research, particularly in complex platform ecosystems where both economic incentives and behavioral factors play a role.
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