In today’s highly connected cyber-physical environments, users are becoming more and more concerned about their privacy and ask for more involvement in the control of their data. However, achieving effective involvement of users requires improving their privacy decision-making. This can be achieved by: (i) raising their awareness regarding the direct and indirect privacy risks they accept to take when sharing data with consumers; (ii) helping them in optimizing their privacy protection decisions to meet their privacy requirements while maximizing data utility. In this article, we address the second goal by proposing a user-centric multi-objective approach for context-aware privacy management in connected environments, denoted δ- Risk . Our approach features a new privacy risk quantification model to dynamically calculate and select the best protection strategies for the user based on her preferences and contexts. Computed strategies are optimal in that they seek to closely satisfy user requirements and preferences while maximizing data utility and minimizing the cost of protection. We implemented our proposed approach and evaluated its performance and effectiveness in various scenarios. The results show that δ- Risk delivers scalability and low-complexity in time and space. Besides, it handles privacy reasoning in real-time, making it able to support the user in various contexts, including ephemeral ones. It also provides the user with at least one best strategy per context.

中文翻译：

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δ- 风险：迈向互联环境中的上下文感知多目标隐私管理

在当今高度连接的网络物理环境中，用户越来越关注自己的隐私，并要求更多地参与对其数据的控制。然而，实现用户的有效参与需要改进他们的隐私决策。这可以通过以下方式实现： (i) 提高他们对在与消费者共享数据时所接受的直接和间接隐私风险的认识；(ii) 帮助他们优化隐私保护决策，以满足他们的隐私要求，同时最大限度地提高数据效用。在本文中，我们通过提出一种以用户为中心的多目标方法来解决第二个目标，用于连接环境中的上下文感知隐私管理，表示为 δ- 风险 . 我们的方法具有一种新的隐私风险量化模型，可以根据用户的偏好和上下文动态计算和选择最佳保护策略。计算策略是最佳的，因为它们寻求密切满足用户需求和偏好，同时最大化数据效用和最小化保护成本。我们实施了我们提出的方法，并评估了它在各种场景中的性能和有效性。结果表明，δ- 风险 在时间和空间上提供可扩展性和低复杂性。此外，它实时处理隐私推理，使其能够在各种情况下为用户提供支持，包括短暂的情况。它还为用户提供每个上下文至少一个最佳策略。