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• W4313446971 abstract "Statistical analysis is a popular research tool used by scientists, industries, and governments to summarize data in a meaningful way. It is an interesting problem to explore how statistical analysis could be performed in a hierarchical environment while preserving the privacy of individuals’ data. Hierarchical identity-based inner product functional encryption (HID-IPFE), introduced by Song et al. (Information Sciences 2021), is a promising cryptographic primitive with applications in the context of privacy-preserving statistical analysis in a hierarchical system. Song et al. provided the construction of a selective chosen-plaintext attack (CPA) secure HID-IPFE scheme in the public key setting based on the q-decisional bilinear Diffie-Hellman exponent (q-DBDHE) problem in the standard model. Motivated by the goal to design HID-IPFE with minimal security assumption, we remove stronger q-type non-standard assumption and propose a selective CPA secure HID-IPFE scheme from the standard decisional bilinear Diffie-Hellman (DBDH) assumption. Our scheme provides stronger security guarantees against selective CPA adversaries compared to the existing q-DBDHE assumption based HID-IPFE. We support the conjectured security of our construction by proper security analysis in the existing security model. Moreover, our scheme offers favorable results in terms of online computation expenses when contrasted with the previous work." @default.
• W4313446971 created "2023-01-06" @default.
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• W4313446971 date "2022-01-01" @default.
• W4313446971 modified "2023-10-05" @default.
• W4313446971 title "Hierarchical Identity Based Inner Product Functional Encryption for Privacy Preserving Statistical Analysis Without q-type Assumption" @default.
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• W4313446971 doi "https://doi.org/10.1007/978-3-031-23098-1_7" @default.
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