Under the auspices of the Computational Complexity Foundation (CCF)

REPORTS > KEYWORD > DIFFERENTIAL PRIVACY:
Reports tagged with differential privacy:
TR11-057 | 15th April 2011

#### Testing and Reconstruction of Lipschitz Functions with Applications to Data Privacy

Revisions: 2

A function $f : D \to R$ has Lipschitz constant $c$ if $d_R(f(x),f(y)) \leq c\cdot d_D(x,y)$ for all $x,y$ in $D$, where $d_R$ and $d_D$ denote the distance functions on the range and domain of $f$, respectively. We say a function is Lipschitz if it has Lipschitz constant 1. (Note ... more >>>

TR11-106 | 6th August 2011
Andrew McGregor, Ilya Mironov, Toniann Pitassi, Omer Reingold, Kunal Talwar, Salil Vadhan

#### The Limits of Two-Party Differential Privacy

We study differential privacy in a distributed setting where two parties would like to perform analysis of their joint data while preserving privacy for both datasets. Our results imply almost tight lower bounds on the accuracy of such data analyses, both for specific natural functions (such as Hamming distance) and ... more >>>

TR12-129 | 9th October 2012
Iftach Haitner, Eran Omri, Hila Zarosim

#### On the Power of Random Oracles

Revisions: 3

In the random oracle model, the parties are given oracle access to a random member of
a (typically huge) function family, and are assumed to have unbounded computational power
(though they can only make a bounded number of oracle queries). This model provides powerful
properties that allow proving the security ... more >>>

TR15-180 | 4th November 2015
Bo Tang, Jiapeng Zhang

#### Barriers to Black-Box Constructions of Traitor Tracing Systems

Reducibility between different cryptographic primitives is a fundamental problem in modern cryptography. As one of the primitives, traitor tracing systems help content distributors recover the identities of users that collaborated in the pirate construction by tracing pirate decryption boxes. We present the first negative result on designing efficient traitor tracing ... more >>>

TR17-168 | 5th November 2017
Amos Beimel, Iftach Haitner, Nikolaos Makriyannis, Eran Omri

#### Tighter Bounds on Multi-Party Coin Flipping, via Augmented Weak Martingales and Di erentially Private Sampling

Revisions: 6

In his seminal work, Cleve [STOC 1986] has proved that any r-round coin-flipping protocol can be efficiently biassed by ?(1/r). The above lower bound was met for the two-party case by Moran, Naor, and Segev [Journal of Cryptology '16], and the three-party case (up to a polylog factor) by Haitner ... more >>>

TR18-071 | 15th April 2018
Iftach Haitner, Kobbi Nissim, Eran Omri, Ronen Shaltiel, Jad Silbak

#### Computational Two-Party Correlation

Revisions: 1

Let $\pi$ be an efficient two-party protocol that given security parameter $\kappa$, both parties output single bits $X_\kappa$ and $Y_\kappa$, respectively. We are interested in how $(X_\kappa,Y_\kappa)$ appears'' to an efficient adversary that only views the transcript $T_\kappa$. We make the following contributions:

\begin{itemize}
\item We develop new tools to ... more >>>

TR18-181 | 30th October 2018
Giuseppe Persiano, Kevin Yeo

#### Lower Bounds for Differentially Private RAMs

In this work, we study privacy-preserving storage primitives that are suitable for use in data analysis on outsourced databases within the differential privacy framework. The goal in differentially private data analysis is to disclose global properties of a group without compromising any individual’s privacy. Typically, differentially private adversaries only ever ... more >>>

TR19-060 | 18th April 2019
Scott Aaronson, Guy Rothblum

#### Gentle Measurement of Quantum States and Differential Privacy

In differential privacy (DP), we want to query a database about $n$ users, in a way that "leaks at most $\varepsilon$ about any individual user," even conditioned on any outcome of the query. Meanwhile, in gentle measurement, we want to measure $n$ quantum states, in a way that "damages the ... more >>>

TR19-081 | 31st May 2019
Iftach Haitner, Noam Mazor, Ronen Shaltiel, Jad Silbak

#### Channels of Small Log-Ratio Leakage and Characterization of Two-Party Differentially Private Computation

Revisions: 1

Consider a PPT two-party protocol ?=(A,B) in which the parties get no private inputs and obtain outputs O^A,O^B?{0,1}, and let V^A and V^B denote the parties’ individual views. Protocol ? has ?-agreement if Pr[O^A=O^B]=1/2+?. The leakage of ? is the amount of information a party obtains about the event {O^A=O^B}; ... more >>>

TR21-124 | 17th August 2021