搜索结果: 1-15 共查到“军队指挥学 differential privacy”相关记录17条 . 查询时间(0.062 秒)
Securely Sampling Biased Coins with Applications to Differential Privacy
distributed differential privacy secure computation
2019/7/17
We design an efficient method for sampling a large batch of dd independent coins with a given bias p∈[0,1]p∈[0,1]. The folklore secure computation method for doing so requires O(λ+logd)O(λ+logd...
We consider the problem of designing scalable, robust protocols for computing statistics about sensitive data. Specifically, we look at how best to design differentially private protocols in a distrib...
Privacy Loss Classes: The Central Limit Theorem in Differential Privacy
differential privacy privacy loss
2018/11/19
In recent years, privacy enhancing technologies have gained tremendous momentum and they are expected to keep a sustained importance. Quantifying the degree of privacy offered by any mechanism working...
Encrypted Databases for Differential Privacy
structured encryption differential privacy statistical databases
2018/11/14
The problem of privatizing statistical databases is a well-studied topic that has culminated with the notion of differential privacy. The complementary problem of securing these databases, however, ha...
Approximate and Probabilistic Differential Privacy Definitions
differential privacy foundations
2018/3/23
This technical report discusses three subtleties related to the widely used notion of differential privacy (DP). First, we discuss how the choice of a distinguisher influences the privacy notion and w...
Risky Traitor Tracing and New Differential Privacy Negative Results
Traitor Tracing Differential Privacy
2017/11/27
Finally, we can capture impossibility results for differential privacy from risky traitor tracing. Since our ciphertexts are short (O(λ)O(λ)), thus we get the negative result which matches what one wo...
Hardness of Non-Interactive Differential Privacy from One-Way Functions
differential privacy one-way functions traitor tracing
2017/11/21
A central challenge in differential privacy is to design computationally efficient noninteractive algorithms that can answer large numbers of statistical queries on a sensitive dataset. That is, we wo...
Privacy Buckets: A numeric method for k-fold tight differential privacy
differential privacy foundations,composition
2017/10/30
The robustness of (approximate) differential privacy (DP) guarantees in the presence of thousands and even hundreds of thousands observations is crucial for many realistic application scenarios, such ...
Concentrated Differential Privacy: Simplifications, Extensions, and Lower Bounds
differential privacy lower bounds
2016/12/10
"Concentrated differential privacy" was recently introduced by Dwork and Rothblum as a relaxation of differential privacy, which permits sharper analyses of many privacy-preserving computations. We pr...
Separating Computational and Statistical Differential Privacy in the Client-Server Model
differential privacy computational differential privacy witness indistinguishability
2016/12/10
Differential privacy is a mathematical definition of privacy for statistical data analysis. It
guarantees that any (possibly adversarial) data analyst is unable to learn too much information
that is...
Achieving Differential Privacy with Bias-Control Limited Source
differential privacy imperfect randomness Bias-Control Limited source
2015/12/31
In the design of differentially private mechanisms, it’s usually
assumed that a uniformly random source is available. However, in many
situations it seems unrealistic, and one must deal with various...
Combining Differential Privacy and Secure Multiparty Computation
secret sharing differential privacy private statistics
2015/12/29
We consider how to perform privacy-preserving analyses on
private data from different data providers and containing personal information
of many different individuals. We combine differential privac...
Differential Privacy in distribution and instance-based noise mechanisms
Anonymity Information hiding
2015/12/25
In this paper, we introduce the notion of (, δ)-differential privacy in distribution,
a strong version of the existing (, δ)-differential privacy, used to mathematically ensure
that private data o...
The Complexity of Computing the Optimal Composition of Differential Privacy
differential privacy composition computational complexity
2015/12/21
In the study of differential privacy, composition theorems (starting with the original
paper of Dwork, McSherry, Nissim, and Smith (TCC’06)) bound the degradation of privacy
when composing several d...
Random Projections, Graph Sparsification, and Differential Privacy
Differential Privacy Graph sparsification
2014/3/10
This paper initiates the study of preserving {\em differential privacy} ({\sf DP}) when the data-set is sparse. We study the problem of constructing efficient sanitizer that preserves {\sf DP} and gua...
