인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
With the rapid development of technologies, treating data with confidentiality protection for cloud systems treating data with the guarantee of confidentiality and integrity, so that analytics are meaningful and responsive, is inevitable. The paper argues that Differential Privacy (DP) and Dynamic Encryption (DE) are two promising complementary approaches to secure the cloud system. Specifically, DP codes inference risk from released results while DE mitigates unauthorized access with fine- grained encryption, key rotation, and limited ciphertexts computation. To be more precise, the paper proposes a deployable using both DP and DE reference architecture for the cloud data platform, with a purpose that realizing the optimization of utility–latency–security. Then experimental evaluation based on aggregate Structured Query Language (SQL) queries and Differentially Private Stochastic Gradient Descent (DP-SGD) training will be briefly outlined. Recent work shows that hour-scale (or continuous) key rotation with moderate privacy budgets is able to provide a pragmatic Pareto balance for common query-heavy applications. Specifically, more homomorphic operations can be applied on low depth aggregates to improve security. By merging these two totally different but beneficial fields, it is believed that an insightful view will be explored on the development of secure-by-design and practically meaningful cloud data platforms.
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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