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Personalized Social Recommendations - Accurate or Private?

Summary: Formalizes accuracy–privacy trade-offs for social-graph-only recommendations under differential privacy, proving utility-loss lower bounds. Adapted private algorithms approach these bounds, but useful recommendations are feasible only for select users or weak privacy guarantees. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
10463
Venue
VLDB
Year
2011
Pagerank
7.1068237e-05
Overall Rank
3,810 | 73.87%
DOI
10.14778/1988776.1988780

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Authors

BibTeX Citation

@article{machanavajjhala_vldb11,
        title = {{Personalized Social Recommendations - Accurate or Private?}},
        author = {Machanavajjhala, Ashwin and Korolova, Aleksandra and Sarma, Atish Das},
        journal = {PVLDB},
        series = {{VLDB} '11},
        volume = {4},
        number = {7},
        pages = {440--451},
        doi = {10.14778/1988776.1988780},
        url = {https://doi.org/10.14778/1988776.1988780},
        year = {2011}
}

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