Privacy-Preserving Top-k Nearest Keyword Search on Outsourced Graphs

Teng, Yiping; Cheng, Xiang; Sen, Sandip; Bi, Rong

doi:10.1109/trustcom.2016.0143

Cited by 3 publications

(4 citation statements)

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“…In this section, we review graph keyword search works that return k best vertices as the desired answers, which can be further divided into two groups based on the specific calculation of scoring function. One group [3,41,48,70,81,109,113] is top-k nearest neighbor keyword search, which considers the distance only when ranking vertices. The other group [1,66,73,111] is top-k relevant neighbor keyword search, which combines both textual relevance and distance.…”

Section: Discussionmentioning

confidence: 99%

“…Clearly, this straightforward approach is inefficient when the size of the graph is large. To avoid such cost-prohibitive computation of shortest paths in query phase, many solutions [3,41,48,70,81,109,113] in the literature are proposed to utilize index structures, which are introduced in detail as below.…”

Section: Top-k Nearest Neighbor Abstractearchmentioning

confidence: 99%

“…For example, keyword search on graphs can threat the privacy of data users, as the graph or the query request may reveal sensitive information such as user's addresses or personal information. Under such circumstances, Teng et al [81] study Problem 3 in a privacypreserving manner, which aims to process -query on graphs without privacy leakage.…”

Section: Top-k Nearest Neighbor Abstractearchmentioning

confidence: 99%

“…• Public-private network [47,81]. In a public-private network, there is a public graph G, containing a set of vertices and a set of edges that are visible to all users.…”

Section: Other Types Of Graphsmentioning

confidence: 99%

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Keyword Search on Large Graphs: A Survey

Yang

Zhang

2021

Data Sci. Eng.

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With the prevalence of Internet access and online services, various big graphs are generated in many real applications (e.g., online social networks and knowledge graphs). An important task on analyzing and mining these graphs is keyword search. Essentially, given a graph G and query Q associated with a set of keywords, the keyword search aims to find a substructure (e.g., rooted tree or subgraph) S in G such that nodes in S collectively cover part of or all keywords in Q, and in the meanwhile, S is optimal on some user specified semantics. Keyword search on graphs can be applied in many real-life applications, such as point-of-interests recommendation and web search facility. In spite of the great importance of graph keyword search, we, however, notice that the latest survey on this topic is far out of date. Consequently, there is prompt need to conduct a comprehensive survey in this research direction. Motivated by this, in this survey, we systematically review graph keyword search studies by classifying the existing works into different categories according to the specific problem definition. This survey aims to provide the researchers a comprehensive understanding of existing graph keyword search solutions.

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Section: Discussionmentioning

confidence: 99%

Section: Top-k Nearest Neighbor Abstractearchmentioning

confidence: 99%

Section: Top-k Nearest Neighbor Abstractearchmentioning

confidence: 99%

“…• Public-private network [47,81]. In a public-private network, there is a public graph G, containing a set of vertices and a set of edges that are visible to all users.…”

Section: Other Types Of Graphsmentioning

confidence: 99%

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