Locating emergencies in a campus using wi-fi access point association data

Abstract

Despite much progress in emergency management, effective techniques for real-time tracking of emergency events are still lacking. We envision a promising direction to achieve real-time emergency tracking is through widely adopted smartphones. In this paper, we explore the first step in achieving this goal, namely, locating emergency in real time using smartphones. Our main contribution is a novel approach that locates emergencies by analyzing AP (access point) association events collected from a campus Wi-Fi network. It is motivated by the observation that human behavior and mobility pattern are significantly altered in the face of emergency, which is reflected in how their smartphones associate with the APs in the network. More specifically, our approach locates emergency by discovering APs with abnormal association patterns using Extreme Value Theory (EVT). Preliminary evaluation using real data collected from a university campus network demonstrates the effectiveness of our approach.

Time‐specific encrypted range query with minimum leakage disclosure

“…The main idea of the trapdoor generation algorithm in enhanced scheme is that every internal node in GGM tree covers a set of leaf nodes, which corresponds to a time range. As an example, in Figure 2, the internal nodes {01, 001} covers the range [2,7]. Therefore, instead of representing time range [2,7] In general, for a time range [a, b], the enhanced scheme finds a set of internal nodes that covers the time range, and uses the set of partial PRF values for the internal nodes (along with their heights) to represent the time range.…”

“…As an example, in Figure 2, the internal nodes {01, 001} covers the range [2,7]. Therefore, instead of representing time range [2,7] In general, for a time range [a, b], the enhanced scheme finds a set of internal nodes that covers the time range, and uses the set of partial PRF values for the internal nodes (along with their heights) to represent the time range. Since there may exist multiple sets of internal nodes that can cover a time range, we will find a set that incurs the minimum trapdoor size, that is containing the minimum number of internal nodes that covers the time range.…”

“…1 If so, computes sum ← sum þ 2 ℓ u i;1 and checks if sum ≥ ϕ, 11. If the inequality holds, outputs 1; otherwise, uses other trapdoor values to follow steps- [7][8][9][10][11]. If all the positions do not contain 1's in the Bloom filter in step [9], derives level-ℓ u i;1 partial PRFs ðx * 1 Þ 1 ; …; ðx * r Þ 1 by using PRG G. This results two level-(ℓ u i;1 À 1) partial PRFs e q and f q such that Gððx * q Þ 1 Þ ¼ e q ‖f q , where q ¼ 1, …, r and follows steps- [7][8][9][10][11] until sum ≥ ϕ and level-0 is obtained from all the partial PRFs in trapdoor.…”

“…Example: Let [4,5] be a range to be encrypted, and let [4,7] be a trapdoor to be searched in enhanced scheme. Also, we assume that threshold ϕ ¼ 2.…”

“…Trap: The proxy uses URC algorithm to build a trapdoor for range [4,7]. The proxy � Computes the partial PRF values as T r ½4;7� ¼ ða…”