2 research outputs found
Broadcast encryption with dealership
In this paper, we introduce a new cryptographic primitive called broadcast encryption with dealership. This notion, which has never been discussed in the cryptography literature, is applicable to many realistic broadcast services, for example subscription-based television service. Specifically, the new primitive enables a dealer to bulk buy the access to some products (e.g., TV channels) from the broadcaster, and hence, it will enable the dealer to resell the contents to the subscribers with a cheaper rate. Therefore, this creates business opportunity model for the dealer. We highlight the security consideration in such a scenario and capture the security requirements in the security model. Subsequently, we present a concrete scheme, which is proven secure under the decisional bilinear Diffie-Hellman exponent and the Diffie-Hellman exponent assumptions
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Secure genotype imputation using homomorphic encryption
Genotype imputation estimates missing genotypes from the haplotype or genotype reference panel in individual genetic sequences, which boosts the potential of genome-wide association and is essential in genetic data analysis. However, the genetic sequences involve people’s privacy, confirming an individual’s identification and even disease information. This work proposes a secure genotype imputation model, which uses a linear regression model and the homomorphic encryption scheme over ciphertext to impute missing genotypes. The inference model is trained with float plaintext parameters, which are round into integers to avoid high complexity homomorphic evaluation on float number operations without bootstrapping operations. Even though the rounding parameters in the inference model are not the same as those in the trained model, We find that it will no effect on the outcome of the homomorphic prediction. Thus, a high-efficiency genotype imputation inference model over the ciphertext is obtained while keeping the high-security level. The simulation results indicate that the accuracy of the secure inference model is almost the same as the original model trained on float parameters. The secure inference model’s accuracy is 98.6% for a single genotype