Eukaryotic translation initiation factors (eIFs) play a central role in potyviral infection. Accordingly, mutations in the gene encoding eIF4E have been identified as a source of recessive resistance in several plant species. In common bean, Phaseolus vulgaris, four recessive genes, bc-1, bc-2, bc-3 and bc-u, have been proposed to control resistance to the potyviruses Bean common mosaic virus (BCMV) and Bean common mosaic necrosis virus. In order to identify molecular entities for these genes, we cloned and sequenced P. vulgaris homologues of genes encoding the eIF proteins eIF4E, eIF(iso)4E and nCBP. Bean genotypes reported to carry bc-3 resistance were found specifically to carry non-silent mutations at codons 53, 65, 76 and 111 in eIF4E. This set of mutations closely resembled a pattern of eIF4E mutations determining potyvirus resistance in other plant species. The segregation of BCMV resistance and eIF4E genotype was subsequently analysed in an F(2) population derived from the P. vulgaris all-susceptible genotype and a genotype carrying bc-3. F(2) plants homozygous for the eIF4E mutant allele were found to display at least the same level of resistance to BCMV as the parental resistant genotype. At 6 weeks after inoculation, all F(2) plants found to be BCMV negative by enzyme-linked immunosorbent assay were found to be homozygous for the mutant eIF4E allele. In F(3) plants homozygous for the mutated allele, virus resistance was subsequently found to be stably maintained. In conclusion, allelic eIF4E appears to be associated with a major component of potyvirus resistance present in bc-3 genotypes of bean.

Decision trees and random forests are widely used classifiers in machine learning. Service providers often host classification models in a cloud service and provide an interface for clients to use the model remotely. While the model is sensitive information of the server, the input query and prediction results are sensitive information of the client. This motivates the need for private decision tree evaluation, where the service provider does not learn the client’s input and the client does not learn the model except for its size and the result. In this work, we identify the three phases of private decision tree evaluation protocols: feature selection, comparison, and path evaluation. We systematize constant-round protocols for each of these phases to identify the best available instantiations using the two main paradigms for secure computation: garbling techniques and homomorphic encryption. There is a natural tradeoff between runtime and communication considering these two paradigms: garbling techniques use fast symmetric-key operations but require a large amount of communication, while homomorphic encryption is computationally heavy but requires little communication. Our contributions are as follows: Firstly, we systematically review and analyse state-of-the-art protocols for the three phases of private decision tree evaluation. Our methodology allows us to identify novel combinations of these protocols that provide better tradeoffs than existing protocols. Thereafter, we empirically evaluate all combinations of these protocols by providing communication and runtime measures, and provide recommendations based on the identified concrete tradeoffs.

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