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Found 23 papers, 4 papers with code
On Differentially Private Online Predictions
In this work we introduce an interactive variant of joint differential privacy towards handling online processes in which existing privacy definitions seem too restrictive.
The Sample Complexity of Distribution-Free Parity Learning in the Robust Shuffle Model
We provide a lowerbound on the sample complexity of distribution-free parity learning in the realizable case in the shuffle model of differential privacy.
Separating Adaptive Streaming from Oblivious Streaming
We present a streaming problem for which every adversarially-robust streaming algorithm must use polynomial space, while there exists a classical (oblivious) streaming algorithm that uses only polylogarithmic space.
Data Structures and Algorithms
On the Round Complexity of the Shuffle Model
Combining this primitive with the two-round semi-honest protocol of Applebaun et al. [TCC 2018], we obtain that every randomized functionality can be computed in the shuffle model with an honest majority, in merely two rounds.
The power of synergy in differential privacy: Combining a small curator with local randomizers
Motivated by the desire to bridge the utility gap between local and trusted curator models of differential privacy for practical applications, we initiate the theoretical study of a hybrid model introduced by "Blender" [Avent et al.,\ USENIX Security '17], in which differentially private protocols of n agents that work in the local-model are assisted by a differentially private curator that has access to the data of m additional users.
Differentially Private Summation with Multi-Message Shuffling
In recent work, Cheu et al. (Eurocrypt 2019) proposed a protocol for $n$-party real summation in the shuffle model of differential privacy with $O_{\epsilon, \delta}(1)$ error and $\Theta(\epsilon\sqrt{n})$ one-bit messages per party.
The Privacy Blanket of the Shuffle Model
Additionally, Erlingsson et al. (SODA 2019) provide a privacy amplification bound quantifying the level of curator differential privacy achieved by the shuffle model in terms of the local differential privacy of the randomizer used by each user.
Private Center Points and Learning of Halfspaces
The building block for this learner is a differentially private algorithm for locating an approximate center point of $m>\mathrm{poly}(d, 2^{\log^*|X|})$ points -- a high dimensional generalization of the median function.
The Limits of Post-Selection Generalization
While statistics and machine learning offers numerous methods for ensuring generalization, these methods often fail in the presence of adaptivity---the common practice in which the choice of analysis depends on previous interactions with the same dataset.
$\mathcal{E}\text{psolute}$: Efficiently Querying Databases While Providing Differential Privacy
In this work we present a model for differentially private outsourced database system and a concrete construction, $\mathcal{E}\text{psolute}$, that provably conceals the aforementioned leakages, while remaining efficient and scalable.
Cryptography and Security
Concentration Bounds for High Sensitivity Functions Through Differential Privacy
We show that differential privacy can be used to prove concentration bounds for such functions in the non-adaptive setting.
Private Incremental Regression
Our first contribution is a generic transformation of private batch ERM mechanisms into private incremental ERM mechanisms, based on a simple idea of invoking the private batch ERM procedure at some regular time intervals.
PSI (Ψ): a Private data Sharing Interface
We provide an overview of PSI ("a Private data Sharing Interface"), a system we are developing to enable researchers in the social sciences and other fields to share and explore privacy-sensitive datasets with the strong privacy protections of differential privacy.
Cryptography and Security Computers and Society Methodology
Locating a Small Cluster Privately
We present a new algorithm for locating a small cluster of points with differential privacy [Dwork, McSherry, Nissim, and Smith, 2006].
Adaptive Learning with Robust Generalization Guarantees
We also show that perfect generalization is a strictly stronger guarantee than differential privacy, but that, nevertheless, many learning tasks can be carried out subject to the guarantees of perfect generalization.
Simultaneous Private Learning of Multiple Concepts
We investigate the direct-sum problem in the context of differentially private PAC learning: What is the sample complexity of solving $k$ learning tasks simultaneously under differential privacy, and how does this cost compare to that of solving $k$ learning tasks without privacy?
Algorithmic Stability for Adaptive Data Analysis
Specifically, suppose there is an unknown distribution $\mathbf{P}$ and a set of $n$ independent samples $\mathbf{x}$ is drawn from $\mathbf{P}$.
Differentially Private Release and Learning of Threshold Functions
Our sample complexity upper and lower bounds also apply to the tasks of learning distributions with respect to Kolmogorov distance and of properly PAC learning thresholds with differential privacy.
On the Generalization Properties of Differential Privacy
Very recently, Bassily et al. presented an improved bound and showed that (a variant of) the private multiplicative weights algorithm can answer $k$ adaptively chosen statistical queries using sample complexity that grows logarithmically in $k$.
Private Learning and Sanitization: Pure vs. Approximate Differential Privacy
We show that the sample complexity of these tasks under approximate differential privacy can be significantly lower than that under pure differential privacy.
Learning Privately with Labeled and Unlabeled Examples
In 2008, Kasiviswanathan et al. (FOCS 2008) gave a generic construction of private learners, in which the sample complexity is (generally) higher than what is needed for non-private learners.
Characterizing the Sample Complexity of Private Learners
Kasiviswanathan et al. gave a generic construction of private learners for (finite) concept classes, with sample complexity logarithmic in the size of the concept class.
What Can We Learn Privately?
Therefore, almost anything learnable is learnable privately: specifically, if a concept class is learnable by a (non-private) algorithm with polynomial sample complexity and output size, then it can be learned privately using a polynomial number of samples.
