7/5/2023 0 Comments Www privacy guard com![]() ![]() Yu, S., Wang, C., Ren, K., Lou, W.: Achieving secure, scalable, and fine-grained data access control in cloud computing. In: Proceedings of the 17th ACM Conference on Computer and Communications Security, CCS 2010, pp. Wang, G., Liu, Q., Wu, J.: Hierarchical attribute-based encryption for fine-grained access control in cloud storage services. In: Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, CCS 2016, pp. ACM, New York (2013)īacis, E., di Vimercati, S.D.C., Foresti, S., Paraboschi, S., Rosa, M., Samarati, P.: Mix&Slice: efficient access revocation in the cloud. In: Proceedings of the Third ACM Conference on Data and Application Security and Privacy, CODASPY 2013, pp. ACM, New York (2016)īates, A., Mood, B., Valafar, M., Butler, K.: Towards secure provenance-based access control in cloud environments. In: Proceedings of the 8th ACM CCS International Workshop on Managing Insider Security Threats, MIST 2016, pp. ACM, New York (2009)ĭesmedt, Y., Shaghaghi, A.: Function-Based Access Control (FBAC): from access control matrix to access control tensor. ![]() ![]() In: Proceedings of the 16th ACM Conference on Computer and Communications Security, CCS 2009, pp. 321–334 (2007)Ĭhase, M., Chow, S.S.M.: Improving privacy and security in multi-authority attribute-based encryption. In: Proceedings of the IEEE Symposium on Security and Privacy, pp. Sahai, A.: Ciphertext-policy attribute-based encryption. In: Proceedings of the 13th ACM Conference on Computer and Communications Security, CCS 2006, pp. Goyal, V., Pandey, O., Sahai, A., Waters, B.: Attribute-based encryption for fine-grained access control of encrypted data. This process is experimental and the keywords may be updated as the learning algorithm improves.Ĭisco visual networking index: Global mobile data traffic forecast update, 2016–2021 white paper. These keywords were added by machine and not by the authors. Addressing the fundamental problem of data usage control, PrivacyGuard will become the cornerstone for free market of private information. Our approach represents a significant departure from traditional privacy protections which often rely on cryptography and pure software-based secure computation techniques. Using remote attestation and TEE, PrivacyGuard ensures that data is only used for the intended purposes approved by the data owner. By encoding data access policy and usage as smart contracts, PrivacyGuard can allow data owners to control who can have what access to their data, and be able to maintain a trustworthy record of their data usage. PrivacyGuard framework seamlessly integrates two new technologies, blockchain and trusted execution environment (TEE). We propose a novel user privacy protection framework, PrivacyGuard, that aims to empower users with full privacy control of their data. While the original intended use of such data is primarily for smart IoT system and device control, the data is often used for other purposes not explicitly consented to by the users. In the current IoT ecosystem, IoT service providers have full control of the collected user data. While IoT promises a more connected and smarter world, this pervasive large-scale data collection, storage, sharing, and analysis raise many privacy concerns. They are also capable of taking smart actions, which are usually from a backend cloud server in the IoT system. Many of these devices are capable of collecting information from individual users and their physical surroundings. In the upcoming evolution of the Internet of Things (IoT), it is anticipated that billions of devices will be connected to the Internet. ![]()
0 Comments
Leave a Reply. |