Share This Author
Calibrating Noise to Sensitivity in Private Data Analysis
The study is extended to general functions f, proving that privacy can be preserved by calibrating the standard deviation of the noise according to the sensitivity of the function f, which is the amount that any single argument to f can change its output.
Evaluating 2-DNF Formulas on Ciphertexts
A homomorphic public key encryption scheme that allows the public evaluation of ψ given an encryption of the variables x1,...,xn and can evaluate quadratic multi-variate polynomials on ciphertexts provided the resulting value falls within a small set.
Smooth sensitivity and sampling in private data analysis
This is the first formal analysis of the effect of instance-based noise in the context of data privacy, and shows how to do this efficiently for several different functions, including the median and the cost of the minimum spanning tree.
Efficient Private Matching and Set Intersection
This work considers the problem of computing the intersection of private datasets of two parties, where the datasets contain lists of elements taken from a large domain, and presents protocols, based on the use of homomorphic encryption and balanced hashing, for both semi-honest and malicious environments.
What Can We Learn Privately?
- S. Kasiviswanathan, Homin K. Lee, Kobbi Nissim, Sofya Raskhodnikova, Adam D. Smith
- Computer Science49th Annual IEEE Symposium on Foundations of…
- 6 March 2008
This work investigates learning algorithms that satisfy differential privacy, a notion that provides strong confidentiality guarantees in the contexts where aggregate information is released about a database containing sensitive information about individuals.
Practical privacy: the SuLQ framework
This work considers a statistical database in which a trusted administrator introduces noise to the query responses with the goal of maintaining privacy of individual database entries, and modify the privacy analysis to real-valued functions f and arbitrary row types, greatly improving the bounds on noise required for privacy.
Revealing information while preserving privacy
A polynomial reconstruction algorithm of data from noisy (perturbed) subset sums and shows that in order to achieve privacy one has to add perturbation of magnitude (Ω√<i>n</i>).
Extending Oblivious Transfers Efficiently
We consider the problem of extending oblivious transfers: Given a small number of oblivious transfers “for free,” can one implement a large number of oblivious transfers? Beaver has shown how to…
Certificate revocation and certificate update
- Kobbi Nissim, M. Naor
- Computer ScienceIEEE Journal on Selected Areas in Communications
- 26 January 1998
This solution represents certificate revocation lists by authenticated dictionaries that support efficient verification whether a certificate is in the list or not and efficient updates and is compatible, e.g., with X.500 certificates.
Practical Locally Private Heavy Hitters
This work presents new practical local differentially private heavy hitters algorithms achieving optimal or near-optimal worst-case error and running time -- TreeHist and Bitstogram and implemented Algorithm TreeHist to verify the theoretical analysis.