Secure multi-keyword search supporting dynamic update and ranked retrieval

2016 ◽  
Vol 13 (10) ◽  
pp. 209-221 ◽  
Author(s):  
Jingbo Yan ◽  
Yuqing Zhang ◽  
Xuefeng Liu

Cloud computing is modern technology as a new computing model in number of business domains. Large numbers of large scale departments are starting to shift the data on to the cloud environment. With the benefit of storage as a service many enterprises are moving their valuable data to the cloud, since it costs less, easily scalable and can be accessed from anywhere any time. Improved dynamic multi-keyword ranking search scheme with top key via encrypted cloud data that simultaneously supports dynamic update operations as deleting and inserting documents. Greedy depth first search algorithm is provided for efficiency multi keywords on place and index structure. Cryptography is one of the establishing trust models. Searchable security is a cryptographic method to provide security. In number of researchers have been working on developing privacy and efficient searchable encryptiontypes. We take new effective cryptographic techniques based on data structures like CRSA and B-Tree to enhance the level of privacy. We propose new multi-keyword search query over encrypted cloud information in retrieving top k scored documents. The vector space model and TFIDF model are used to build index and query generation. This paper focuses on multi keyword search based on ranking over an encrypted cloud data. The search uses the feature of similarity and inner product similarity matching. We propose to support the top-k Multi-full-text search for security and performance analysis show that the proposed model guarantees a high safety and practicality and dynamic update operations, such as deleting and adding documents. The experimental results show that the overhead in computation and communication is low.


1996 ◽  
Vol 35 (04/05) ◽  
pp. 309-316 ◽  
Author(s):  
M. R. Lehto ◽  
G. S. Sorock

Abstract:Bayesian inferencing as a machine learning technique was evaluated for identifying pre-crash activity and crash type from accident narratives describing 3,686 motor vehicle crashes. It was hypothesized that a Bayesian model could learn from a computer search for 63 keywords related to accident categories. Learning was described in terms of the ability to accurately classify previously unclassifiable narratives not containing the original keywords. When narratives contained keywords, the results obtained using both the Bayesian model and keyword search corresponded closely to expert ratings (P(detection)≥0.9, and P(false positive)≤0.05). For narratives not containing keywords, when the threshold used by the Bayesian model was varied between p>0.5 and p>0.9, the overall probability of detecting a category assigned by the expert varied between 67% and 12%. False positives correspondingly varied between 32% and 3%. These latter results demonstrated that the Bayesian system learned from the results of the keyword searches.


2019 ◽  
Vol 118 (1) ◽  
pp. 36-41
Author(s):  
Jung-Woo Lee ◽  
Seung-Cheon Kim ◽  
Sung-Hoon Kim ◽  
Jin-Ho Lim

Background/Objectives: In this study, research to improve efficiency of online advertising market, we would like to propose a new performance index called "Leakage Ratio" which can increase the efficiency of advertisement. Methods/Statistical analysis: Naver, the Internet portal site in Korea, is the most influential medium for online keyword search advertising. In this study, Leakage Ratio management is applied to online keyword search ads for five medium and large size online shopping malls at Naver. Based on the performance trend of each search keyword, we tried to improve the efficiency of the whole advertisement by changing the bid of the low efficiency keyword.


2019 ◽  
Vol 118 (8) ◽  
pp. 308-314
Author(s):  
Jung-Woo Lee ◽  
Seung- Cheon ◽  
Sung-Hoon Kim ◽  
Jin-Ho Lim

In this study, research to improve efficiency of online advertising market, we would like to propose a new performance index called "Leakage Ratio" which can increase the efficiency of advertisement. Methods/Statistical analysis: Naver, the Internet portal site in Korea, is the most influential medium for online keyword search advertising. In this study, Leakage Ratio management is applied to online keyword search ads for five medium and large size online shopping malls at Naver. Based on the performance trend of each search keyword, we tried to improve the efficiency of the whole advertisement by changing the bid of the low efficiency keyword.


2020 ◽  
Author(s):  
Tom Joseph Barry ◽  
David John Hallford ◽  
Keisuke Takano

Decades of research has examined the difficulty that people with psychiatric diagnoses, such as Major Depressive Disorder, Schizophrenia Spectrum Disorders, and Posttraumatic Stress Disorder, have in recalling specific autobiographical memories from events that lasted less than a day. Instead, they seem to retrieve general events that have occurred many times or which occurred over longer periods of time, termed overgeneral memory. We present the first transdiagnostic meta-analysis of memory specificity/overgenerality, and the first meta-regression of proposed causal mechanisms. A keyword search of Embase, PsycARTICLES and PsycINFO databases yielded 74 studies that compared people with and without psychiatric diagnoses on the retrieval of specific (k = 85) or general memories (k = 56). Multi-level meta-analysis confirmed that people with psychiatric diagnoses typically recall fewer specific (g = -0.864, 95% CI[-1.030, -0.698]) and more general (g = .712, 95% CI[0.524, 0.900]) memories than diagnoses-free people. The size of these effects did not differ between diagnostic groups. There were no consistent moderators; effect sizes were not explained by methodological factors such as cue valence, or demographic variables such as participants’ age. There was also no support for the contribution of underlying processes that are thought to be involved in specific/general memory retrieval (e.g., rumination). Our findings confirm that deficits in autobiographical memory retrieval are a transdiagnostic factor associated with a broad range of psychiatric problems, but future research should explore novel causal mechanisms such as encoding deficits and the social processes involved in memory sharing and rehearsal.


2012 ◽  
Vol 35 (11) ◽  
pp. 2237 ◽  
Author(s):  
Jun HU ◽  
Ju FAN ◽  
Guo-Liang LI ◽  
Shan-Shan CHEN

2010 ◽  
Vol 30 (9) ◽  
pp. 2335-2338
Author(s):  
Hua-yun YAN ◽  
Ji-hong GUAN
Keyword(s):  

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