Local differential privacy for unbalanced multivariate nominal attributes
| Autor: | Chiping Zhang, Xuejie Feng |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Scheme (programming language)
Multivariate statistics Mathematical optimization General Computer Science Computer science Cardano formula Lagrange multiplier 02 engineering and technology computer.software_genre lcsh:QA75.5-76.95 News aggregator symbols.namesake Information 0202 electrical engineering electronic engineering information engineering lcsh:Information theory Differential privacy computer.programming_language 020206 networking & telecommunications Extension (predicate logic) lcsh:Q350-390 Nonlinear system symbols 020201 artificial intelligence & image processing lcsh:Electronic computers. Computer science Convex function computer Multivariate |
| Zdroj: | Human-Centric Computing and Information Sciences, Vol 10, Iss 1, Pp 1-21 (2020) |
| ISSN: | 2192-1962 |
| Popis: | Data with unbalanced multivariate nominal attributes collected from a large number of users provide a wealth of knowledge for our society. However, it also poses an unprecedented privacy threat to participants. Local differential privacy, a variant of differential privacy, is proposed to eliminate the privacy concern by aggregating only randomized values from each user, with the provision of plausible deniability. However, traditional local differential privacy algorithms usually assign the same privacy budget to attributes with different dimensions, leading to large data utility loss and high communication costs. To obtain highly accurate results while satisfying local differential privacy, the aggregator needs a reasonable privacy budget allocation scheme. In this paper, the Lagrange multiplier (LM) algorithm was used to transform the privacy budget allocation problem into a problem of calculating the minimum value from unconditionally constrained convex functions. The solution to the nonlinear equation obtained by the Cardano formula (CF) and Newton-Raphson (NS) methods was used as the optimal privacy budget allocation scheme. Then, we improved two popular local differential privacy mechanisms by taking advantage of the proposed privacy budget allocation techniques. Extension simulations on two different data sets with multivariate nominal attributes demonstrated that the scheme proposed in this paper can significantly reduce the estimation error under the premise of satisfying local differential privacy. |
| Databáze: | OpenAIRE |
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