Privacy is an individual choice to determine which personal details can be collected, used and shared. Individual consent and transparency are the core tenets for earning customers trust and this motivates the organizations to adopt privacy enhancing practices while creating the systems. The goal of a privacy-aware design is to protect information in a way that does not increase an adversary's existing knowledge about an individual beyond what is permissible. This becomes critical when these data elements can be linked with the wealth of auxiliary information available outside the system to identify an individual. Privacy regulations around the world provide directives to protect individual privacy but are generally complex and vague, making their translation into actionable and technical privacy-friendly architectures challenging. In this paper, we utilize Shannon's Entropy to create an objective metric that can help simplify the state-of-the-art Privacy Design Strategies proposed in the literature and aid our key technical design decisions to create privacy aware architectures.

中文翻译：

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利用香农熵创建隐私感知架构

隐私是个人选择，以确定可以收集、使用和共享哪些个人详细信息。个人同意和透明度是赢得客户信任的核心原则，这促使组织在创建系统时采用增强隐私的做法。隐私意识设计的目标是以一种不会增加对手对个人的现有知识超出允许范围的方式来保护信息。当这些数据元素可以与系统外可用的大量辅助信息相关联以识别个人时，这变得至关重要。世界各地的隐私法规提供了保护个人隐私的指令，但通常都很复杂和模糊，这使得将其转化为可操作且技术隐私友好的架构具有挑战性。