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Connect with Your Friends and Share Private Information Safely

机译:与您的朋友联系并安全共享私人信息

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摘要

With the advent of ubiquitous computing and social networking, we often attempt to connect to friend of friends (multi-hop friends) and share private information. However, without explicitly being convinced about the trustworthiness of the entire process, private information may be leaked and this causes users to get shy away from participating in such activities where private information requires to be disclosed. In view of that, we propose a privacy preserving trust management scheme where multi-hop trust is established in an effective way. The scheme consists of two parts. In first part, we formulate a way of trust score computation. In second part, we propose an algorithm for discovering the friend for sharing private data using the concept of Ant Colony Optimization (ACO) based meta-heuristics, which achieves privacy preserving search strategy. It is a kind of learning distributed parameters and is simple to implement but efficient in outcome. The advantage of our scheme is that it is highly scalable and the participation of the central server is minimal. We consider the communication part of ubiquitous computing consists of tiny sensor nodes forming Wireless Sensor Networks (WSNs) like that in smart car or smart city.
机译:随着无处不在的计算和社交网络的出现,我们经常尝试与朋友的朋友(多跳朋友)建立联系并共享私人信息。但是,如果没有明确地相信整个过程的可信赖性,则可能会泄露私人信息,这会使用户避开参加需要披露私人信息的活动。有鉴于此,我们提出了一种隐私保护信任管理方案,其中以有效方式建立了多跳信任。该计划包括两个部分。在第一部分中,我们制定了一种信任分数计算的方法。在第二部分中,我们提出了一种基于基于蚁群优化(ACO)的元启发式算法来发现共享共享数据的朋友的算法,该算法实现了隐私保护搜索策略。它是一种学习分布式参数的方法,易于实现,但结果有效。我们的方案的优点是它具有高度的可扩展性,并且中央服务器的参与很少。我们认为无处不在的计算的通信部分由微小的传感器节点组成,这些节点形成像智能汽车或智慧城市中的无线传感器网络(WSN)。

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