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An introduction to zero-knowledge machine learning (ZKML)

Zero-Knowledge machine learning (ZKML) is a field of research and
development that has been making waves in cryptography circles recently. But
what is it and why is it useful? First, let's break down the term into its
two constituents and explain what they are. ....

Service Mlaa , Modulus Labs , Proving Machine Learning Inference , Venn Diagram , Homomorphic Encryption , Aztec Protocol ,

"Chosen-Ciphertext Secure Homomorphic Proxy Re-Encryption" by Fucai Luo, Saif Al-Kuwari et al.

Homomorphic Proxy Re-Encryption (HPRE) is an extension of Proxy Re-Encryption (PRE) which combines the advantages of both Homomorphic Encryption (HE) and PRE. A HPRE scheme allows arbitrary evaluations to be performed on ciphertexts under one (the delegator's) public key and, using a re-encryption key, it transforms the resulting ciphertext to a new ciphertext under another (the delegatee's) public key. Prior HPRE schemes are either CPA-secure or CCA-secure but only support partial homomorphic operations. We propose a generic construction of single-hop HPRE scheme which supports fully homomorphic operations. The proposed scheme is proven secure in our new index-based CCA-HPRE model. Our technique is to give a generic transformation that turns any multi-identity identity-based FHE (IBFHE) scheme with key switching into Fully Homomorphic Encryption (FHE) with key switching from which we can obtain the proposed single-hop HPRE scheme. We also present a concrete instantiation of ....

Proxy Re Encryption , Homomorphic Encryption , Fully Homomorphic Encryption , Ulti Identity Ibfhe ,

"Privacy-preserving anomaly counting for time-series data in edge-assis" by Shijin Chen, Willy Susilo et al.

Crowdsensing is an emerging data collection paradigm that enables data collected from a large number of Internet of Things devices to support effective decision-making. Anomaly counting as a data analysis method allows the identification of unintended behaviors to enhance decision-making capabilities. However, ensuring the sensing data privacy and increasing the willingness of data providers are significant challenges to guarantee quality decision-making. This paper proposes a flexible mechanism to provide the service of privacy-preserving anomaly counting for time-series data in edge-assisted crowdsensing. Specifically, to protect the sensing data of the data providers, a secure secret sharing protocol is designed based on additive secret sharing. Next, a privacy-preserving anomaly counting algorithm based on the windowed Gaussian anomaly detector is proposed, and multiple secure sub-protocols are employed as building blocks to guarantee the privacy of the counting result and the sens ....

Anomaly Detection , Homomorphic Encryption , Privacy Protection , Time Series Data ,