Cite this DOI

Katragadda, B., & Shari, S. (2018). Supporting Privacy Protection in Personalized Web Search. *Journal of Science & Technology*, *03*(01), 17–21. https://doi.org/10.46243/jstj.2018.v3.i1.159

@article{katragadda2018supporting,
  author    = {Katragadda, Brahmaji and Shari, Sk.Meera},
  title     = {{Supporting Privacy Protection in Personalized Web Search}},
  journal   = {Journal of Science \& Technology},
  year      = {2018},
  month     = {jan},
  volume    = {03},
  number    = {01},
  pages     = {17--21},
  publisher = {Longman Publishers},
  issn      = {2456-5660},
  doi       = {10.46243/jstj.2018.v3.i1.159},
  url       = {https://doi.org/10.46243/jstj.2018.v3.i1.159},
  language  = {en},
  abstract  = {The potency of Personalized web search (PWS) inenhancing the quality of diverse search services on the Internetis authenticated. Nevertheless, user’s disinclination to unfold their private information in the course of their search has created a vitalstop for the proliferation of PWS. We aspire to propose a PWS framework called UPS. while valuing user specified privacy requirements, with the help of queries, this can adaptively generalize profiles.This technique aims at maintaining equilibrium between two predictive metrics that gauges the utility of personalization and the privacy risk of uncovering the generalized profile. GreedyDP and GreedyIL are the two greedy algorithms for runtime generalization are proposed here. Additionally, weimpart an online prediction mechanism for deciding whether personalizing a query is serviceable. Extensive experiments demonstrate the effectiveness ofour framework. The experimental results also reveal that GreedyIL outstandingly surpasses GreedyDP in terms of efficiency.}
}

TY  - JOUR
TI  - Supporting Privacy Protection in Personalized Web Search
AU  - Katragadda, Brahmaji
AU  - Shari, Sk.Meera
JO  - Journal of Science & Technology
PY  - 2018
DA  - 2018/01/03/
VL  - 03
IS  - 01
SP  - 17
EP  - 21
PB  - Longman Publishers
SN  - 2456-5660
LA  - en
AB  - The potency of Personalized web search (PWS) inenhancing the quality of diverse search services on the Internetis authenticated. Nevertheless, user’s disinclination to unfold their private information in the course of their search has created a vitalstop for the proliferation of PWS. We aspire to propose a PWS framework called UPS. while valuing user specified privacy requirements, with the help of queries, this can adaptively generalize profiles.This technique aims at maintaining equilibrium between two predictive metrics that gauges the utility of personalization and the privacy risk of uncovering the generalized profile. GreedyDP and GreedyIL are the two greedy algorithms for runtime generalization are proposed here. Additionally, weimpart an online prediction mechanism for deciding whether personalizing a query is serviceable. Extensive experiments demonstrate the effectiveness ofour framework. The experimental results also reveal that GreedyIL outstandingly surpasses GreedyDP in terms of efficiency.
DO  - 10.46243/jstj.2018.v3.i1.159
UR  - https://doi.org/10.46243/jstj.2018.v3.i1.159
ER  -

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    "DOI": "10.46243/jstj.2018.v3.i1.159",
    "URL": "https://doi.org/10.46243/jstj.2018.v3.i1.159",
    "title": "Supporting Privacy Protection in Personalized Web Search",
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    "publisher": "Longman Publishers",
    "language": "en",
    "abstract": "The potency of Personalized web search (PWS) inenhancing the quality of diverse search services on the Internetis authenticated. Nevertheless, user’s disinclination to unfold their private information in the course of their search has created a vitalstop for the proliferation of PWS. We aspire to propose a PWS framework called UPS. while valuing user specified privacy requirements, with the help of queries, this can adaptively generalize profiles.This technique aims at maintaining equilibrium between two predictive metrics that gauges the utility of personalization and the privacy risk of uncovering the generalized profile. GreedyDP and GreedyIL are the two greedy algorithms for runtime generalization are proposed here. Additionally, weimpart an online prediction mechanism for deciding whether personalizing a query is serviceable. Extensive experiments demonstrate the effectiveness ofour framework. The experimental results also reveal that GreedyIL outstandingly surpasses GreedyDP in terms of efficiency.",
    "ISSN": "2456-5660"
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From the record as registered (version 2) — the record and its history. Programs: https://registry.smartscholars.in/api.php?action=cite&doi=10.46243%2Fjstj.2018.v3.i1.159 gives all four in one JSON answer.