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Volumn 9562, Issue , 2016, Pages 157-175

The complexity of computing the optimal composition of differential privacy

Author keywords

Approximation algorithms; Composition; Computational complexity; Differential privacy

Indexed keywords

APPROXIMATION ALGORITHMS; CHEMICAL ANALYSIS; COMBINATORIAL OPTIMIZATION; COMPUTATIONAL COMPLEXITY; CRYPTOGRAPHY; POLYNOMIAL APPROXIMATION;

EID: 84952690453     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-662-49096-9_7     Document Type: Conference Paper
Times cited : (111)

References (11)
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    • Calibrating noise to sensitivity in private data analysis
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    • Dwork, C., McSherry, F., Nissim, K., Smith, A.: Calibrating noise to sensitivity in private data analysis. In: Halevi, S., Rabin, T. (eds.) TCC 2006. LNCS, vol. 3876, pp. 265-284. Springer, Heidelberg (2006).
    • (2006) TCC 2006. LNCS , vol.3876 , pp. 265-284
    • Dwork, C.1    McSherry, F.2    Nissim, K.3    Smith, A.4
  • 4
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    • The algorithmic foundations of differential privacy
    • Dwork, C., Roth, A.: The algorithmic foundations of differential privacy. Found. Trends Theor. Comput. Sci. 9(3-4), 211-407 (2013).
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.