When Bad News Become Good News
نویسندگان
چکیده
Hard physical learning problems have been introduced as an alternative option to implement cryptosystems based on hard problems. Their high-level idea is use inexact computing generate erroneous computations directly, rather than first compute correctly and add errors afterwards. Previous works focused the applicability of this Learning Parity with Noise (LPN) problem a step, formalized it Physical (LPPN). In work, we generalize With Errors (LWE) problem, (LWPE). We show that direct application design ideas used for LPPN prototypes leads new source (mathematical) data dependencies in error distributions can reduce security underlying problem. then tweaks be avoid issue, making LWPE samples natively robust against such dependencies. additionally put forward these open quite wide space could make relevant various applications. And conclude by presenting prototype FPGA confirming our claims.
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ژورنال
عنوان ژورنال: IACR transactions on cryptographic hardware and embedded systems
سال: 2022
ISSN: ['2569-2925']
DOI: https://doi.org/10.46586/tches.v2022.i4.1-24