Wu, Z.; Li, R.; Zhou, Z.; Guo, J.; Jiang, J.; Su, X.: ¬A user sensitive subject protection approach for book search service (2020)
0.02
0.01716654 = product of:
0.051499616 = sum of:
0.051499616 = product of:
0.07724942 = sum of:
0.047734402 = weight(_text_:network in 5617) [ClassicSimilarity], result of:
0.047734402 = score(doc=5617,freq=2.0), product of:
0.19402927 = queryWeight, product of:
4.4533744 = idf(docFreq=1398, maxDocs=44218)
0.043569047 = queryNorm
0.2460165 = fieldWeight in 5617, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
4.4533744 = idf(docFreq=1398, maxDocs=44218)
0.0390625 = fieldNorm(doc=5617)
0.029515022 = weight(_text_:22 in 5617) [ClassicSimilarity], result of:
0.029515022 = score(doc=5617,freq=2.0), product of:
0.15257138 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.043569047 = queryNorm
0.19345059 = fieldWeight in 5617, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.0390625 = fieldNorm(doc=5617)
0.6666667 = coord(2/3)
0.33333334 = coord(1/3)
- Abstract
- In a digital library, book search is one of the most important information services. However, with the rapid development of network technologies such as cloud computing, the server-side of a digital library is becoming more and more untrusted; thus, how to prevent the disclosure of users' book query privacy is causing people's increasingly extensive concern. In this article, we propose to construct a group of plausible fake queries for each user book query to cover up the sensitive subjects behind users' queries. First, we propose a basic framework for the privacy protection in book search, which requires no change to the book search algorithm running on the server-side, and no compromise to the accuracy of book search. Second, we present a privacy protection model for book search to formulate the constraints that ideal fake queries should satisfy, that is, (i) the feature similarity, which measures the confusion effect of fake queries on users' queries, and (ii) the privacy exposure, which measures the cover-up effect of fake queries on users' sensitive subjects. Third, we discuss the algorithm implementation for the privacy model. Finally, the effectiveness of our approach is demonstrated by theoretical analysis and experimental evaluation.
- Date
- 6. 1.2020 17:22:25
