版權(quán)說明:本文檔由用戶提供并上傳,收益歸屬內(nèi)容提供方,若內(nèi)容存在侵權(quán),請(qǐng)進(jìn)行舉報(bào)或認(rèn)領(lǐng)
文檔簡(jiǎn)介
1、2400 英文單詞, 英文單詞,1.3 萬英文字符,中文 萬英文字符,中文 4300 字文獻(xiàn)出處: 文獻(xiàn)出處:Malthankar S V , Kolte S . Client Side Privacy Protection Using Personalized Web Search[J]. Procedia Computer Science, 2016, 79:1029-1035.Client side Privacy Protect
2、ion Using Personalized Web SearchMrs. Sharvari V. Malthankar , Prof. Shilpa KolteAbstractWe are providing a Client-side privacy protection for personalized web search.. Any PWS captures user profiles in a hierarchical ta
3、xonomy. The system is performing online generalization on user profiles to protect the personal privacy without compromising the search quality and attempt to improve the search quality with the personalization utility o
4、f the user profile. On other side they need to hide the privacy contents existing in the user profile to place the privacy risk under control. User privacy can be provided in form of protection like without compromising
5、the personalized search quality. In general we are working for a trade off between the search quality and the level of privacy protection achieved from generalization.Keywords: UPS,Privacy Protection, Greedy DP,Greedy I
6、L;1. IntroductionThe web search engine has long become the most important portal for ordinary people looking for useful information on the web. users may experience failure when search engines return irrelevant results
7、that do not meet their real and expected intentions. Such irrelevant think is largely due to the enormous variety of users’ contexts and backgrounds, as well as the ambiguity of the texts. Personalized web search (PWS) i
8、s one general search techniques aiming to providing better search results, which are tailored to individual user needs. At the expense, user information has to be collected and analyzed to figure out the user intention b
9、ehind the issued query.PWS can generally into two types● Click-log-based methods and● Profile-based methodsIn Click-log-based methods we found as● They simply impose bias to clicked pages in the user’s query history.● It
10、 can only work on repeated queries from the same user, which is a strong limitation confining its applicability.In Profile-based methods we found as● Profile-based methods can be potentially effective forAlmost all sorts
11、 of queries, but are reported to be Unstable under some circumstances.● Improve the search experience with complicated user-interest models generated from user profiling techniques.● PWS has demonstrated more effective i
12、n improving the quality of web search recently, with increasing usage of personal and behavior information to profile its users, which is usually gathered implicitly from query history, browsing history, click-through da
13、ta bookmarks, user documents and so forth.2. LITERATURE SURVEY Profile-Based PersonalizationMany profile representations are available in the literature to facilitate different personalization strategies. Earlier techniq
14、ues utilize term lists/vectors or bag of words to represent their profile. However, most recent works build profiles in hierarchical structures due to their Two predictive metrics utility of personalization and the priva
15、cy risk are used for build – up of the profile. In the generalization process we use greedy DP and the greedy IL algorithm. The innovative outcome tells that greedy IL obviously outperforms greedy DP in terms of efficien
16、cy [8].● We propose a method that, given a query submitted to a search engine, suggests a list of related queries. The related queries are based on previously issued queries, and can be issued by the user to the search
17、engine to tune or redirect the search process. The method proposed is based on a query clustering process in a group of semantically similar queries are identified. [9].● We proposed the reliability of implicit feedback
18、generated from click through data in WWW search. Analyzing the users’decision process using eye tracking and comparing implicit feedback against manual relevance judgments, we conclude that clicks are informative but bia
19、sed. We show that relative preferences derived from clicks are reasonably accurate on average [10].● We propose a novel context-aware query suggestion approach.. In which steps for in an offline model learning step, to a
20、ddress data sparseness, queries are summarized into concepts by clustering a click- through bipartite. Then, from session data a concept sequence suffix tree is constructed as the query suggestion model [11].3. PROBLEM
21、DEFINITIONTo protect user privacy in profile-based PWS, we have to consider two contradicting effects during the search process. On the one hand, they attempt to improve the search quality with the personalization utilit
22、y of the user profile. They need to hide the privacy contents in existing user profile to place the privacy risk under control. Significant gains can be obtained by personalization at the expense of only a small and less
23、-sensitive portion of the user profile, namely a generalized profile. Thus, user privacy can be protected without compromising the personalized search qualityThe existing profile-based Personalized Web Search does not su
24、pport runtime profiling. A user profile is typically generalized for only once offline, and used to personalize all queries from a same user indiscriminatingly. Such “one profile is fits all” strategy certainly has drawb
25、acks given the variety of queries. One evidence reported in is that profile-based personalization may not even help to improve the search quality for some ad hoc queries, though exposing user profile to a server has put
26、the user’s privacy at risk.The existing methods do not take into account of the customization of privacy requirements. This makes some user privacy to be overprotected while others insufficiently protected. For example,
27、in all the sensitive topics are detected using an absolute metric called surprise based on the information theory, assuming that the interests with less user document support are more sensitive. However, this assumption
28、can be doubted with a simple counter.Many personalization techniques require iterative user interactions when creating personalized search results. We require to refine the search results with some metrics which require
29、multiple user interactions, such as rank scoring, average rank, and so on. This paradigm is however, infeasible for runtime profiling, as it will not only pose too much risk of privacy breach, but also demand prohibitive
30、 processing time for profiling. Thus, we need predictive metrics to measure the search quality and breach risk after personalization, without incurring iterative user interaction.Disadvantage are as follows:● All the sen
溫馨提示
- 1. 本站所有資源如無特殊說明,都需要本地電腦安裝OFFICE2007和PDF閱讀器。圖紙軟件為CAD,CAXA,PROE,UG,SolidWorks等.壓縮文件請(qǐng)下載最新的WinRAR軟件解壓。
- 2. 本站的文檔不包含任何第三方提供的附件圖紙等,如果需要附件,請(qǐng)聯(lián)系上傳者。文件的所有權(quán)益歸上傳用戶所有。
- 3. 本站RAR壓縮包中若帶圖紙,網(wǎng)頁內(nèi)容里面會(huì)有圖紙預(yù)覽,若沒有圖紙預(yù)覽就沒有圖紙。
- 4. 未經(jīng)權(quán)益所有人同意不得將文件中的內(nèi)容挪作商業(yè)或盈利用途。
- 5. 眾賞文庫(kù)僅提供信息存儲(chǔ)空間,僅對(duì)用戶上傳內(nèi)容的表現(xiàn)方式做保護(hù)處理,對(duì)用戶上傳分享的文檔內(nèi)容本身不做任何修改或編輯,并不能對(duì)任何下載內(nèi)容負(fù)責(zé)。
- 6. 下載文件中如有侵權(quán)或不適當(dāng)內(nèi)容,請(qǐng)與我們聯(lián)系,我們立即糾正。
- 7. 本站不保證下載資源的準(zhǔn)確性、安全性和完整性, 同時(shí)也不承擔(dān)用戶因使用這些下載資源對(duì)自己和他人造成任何形式的傷害或損失。
最新文檔
- [雙語翻譯]網(wǎng)絡(luò)隱私保護(hù)外文翻譯--使用個(gè)性化網(wǎng)絡(luò)搜索的客戶端隱私保護(hù)(英文)
- [雙語翻譯]網(wǎng)絡(luò)隱私保護(hù)外文翻譯--使用個(gè)性化網(wǎng)絡(luò)搜索的客戶端隱私保護(hù)中英全
- 2016年網(wǎng)絡(luò)隱私保護(hù)外文翻譯--使用個(gè)性化網(wǎng)絡(luò)搜索的客戶端隱私保護(hù)
- 2016年網(wǎng)絡(luò)隱私保護(hù)外文翻譯--使用個(gè)性化網(wǎng)絡(luò)搜索的客戶端隱私保護(hù).DOCX
- 2016年網(wǎng)絡(luò)隱私保護(hù)外文翻譯--使用個(gè)性化網(wǎng)絡(luò)搜索的客戶端隱私保護(hù)(英文).PDF
- 社會(huì)網(wǎng)絡(luò)個(gè)性化隱私保護(hù)技術(shù)研究.pdf
- 個(gè)性化搜索中的隱私安全保護(hù)框架.pdf
- 個(gè)性化搜索中隱私保護(hù)的問題研究.pdf
- 社會(huì)網(wǎng)絡(luò)個(gè)性化隱私保護(hù)方法的研究與實(shí)現(xiàn).pdf
- 個(gè)性化搜索中隱私保護(hù)的關(guān)鍵問題研究.pdf
- 社會(huì)網(wǎng)絡(luò)數(shù)據(jù)發(fā)布中個(gè)性化隱私保護(hù)方法的研究.pdf
- 客戶端個(gè)性化信息搜索服務(wù)研究.pdf
- [雙語翻譯]隱私權(quán)外文翻譯--現(xiàn)代技術(shù)與隱私權(quán)保護(hù)面臨的挑戰(zhàn)
- [雙語翻譯]隱私權(quán)外文翻譯--現(xiàn)代技術(shù)與隱私權(quán)保護(hù)面臨的挑戰(zhàn)(英文)
- 社區(qū)化網(wǎng)絡(luò)中的隱私保護(hù).pdf
- 移動(dòng)社交環(huán)境下的個(gè)性化位置隱私保護(hù).pdf
- 電子商務(wù)個(gè)性化和在線隱私保護(hù)功能以旅游網(wǎng)站為例【外文翻譯】
- 基于客戶端及模糊網(wǎng)絡(luò)代理的個(gè)性化搜索引擎的研究.pdf
- 實(shí)現(xiàn)個(gè)性化隱私保護(hù)的微聚集算法研究.pdf
- 從人肉搜索看網(wǎng)絡(luò)隱私權(quán)保護(hù).pdf
評(píng)論
0/150
提交評(píng)論