메뉴 건너뛰기
.. 내서재 .. 알림
소속 기관/학교 인증
인증하면 논문, 학술자료 등을  무료로 열람할 수 있어요.
한국대학교, 누리자동차, 시립도서관 등 나의 기관을 확인해보세요
(국내 대학 90% 이상 구독 중)
로그인 회원가입 고객센터 ENG
주제분류

추천
검색

논문 기본 정보

자료유형
학술저널
저자정보
김종길 (이화여자대학교)
저널정보
한국차세대컴퓨팅학회 한국차세대컴퓨팅학회 논문지 한국차세대컴퓨팅학회 논문지 제19권 제2호
발행연도
2023.4
수록면
100 - 113 (14page)

이용수

표지
📌
연구주제
📖
연구배경
🔬
연구방법
🏆
연구결과
AI에게 요청하기
추천
검색

초록· 키워드

오류제보하기
In this paper, we propose channel based encryption with identity-based traceability for decentralized networks such as fog/edge networks. In a decentralized network, controlling access to data is difficult as the data are transmitted via multiple other nodes. Particularly, when data is transmitted using a protocol that has multiple legitimate recipients like a publish/subscribe protocol, tracing who has accessed broadcast data is difficult. To resolve this problem, we present a new encryption scheme that enables us to trace the data flow by confirming the identity of the actual recipient. We construct a channel-based encryption scheme tracing the data flow, which is suitable for a publish/subscribe protocol, based on revocable attribute-based encryption (ABE). We, then, set the edge server to preprocess the ciphertext by re-encrypting and partially decrypting the ciphertext for the actual recipient. Prior to our work, revocable attribute-based broadcast encryption (ABE) was used only to revoke users by re-encryption. However, this method only allows us to trace recipients after the ciphertext is re-encrypted. That means that the data before reaching the re-encryption oracle, that is the edge server in our proposed system, can be accessed by other recipients without leaving any trace and this makes tracing the data flow difficult. In our proposed scheme, the ciphertext cannot be decrypted only after it is partially decrypted by the edge server. Therefore, tracing the data flow is possible via the edge server. We provide proof of security using the Decisional Diffie-Hellman assumption in the paper.

목차

등록된 정보가 없습니다.

참고문헌 (0)

참고문헌 신청

함께 읽어보면 좋을 논문

논문 유사도에 따라 DBpia 가 추천하는 논문입니다. 함께 보면 좋을 연관 논문을 확인해보세요!

이 논문의 저자 정보

최근 본 자료

전체보기

댓글(0)

0