• Publications
  • Influence
Design Tradeoffs for SSD Performance
TLDR
We show that SSD performance and lifetime is highly workload-sensitive, and that complex systems problems that normally appear higher in the storage stack, or even in distributed systems are relevant to device firmware. Expand
Consistent hashing and random trees: distributed caching protocols for relieving hot spots on the World Wide Web
TLDR
We describe a family of caching protocols for distrib-uted networks that can be used to decrease or eliminate the occurrence of hot spots in the network. Expand
The smallest grammar problem
This paper addresses the smallest grammar problem: What is the smallest context-free grammar that generates exactly one given string /spl sigma/? This is a natural question about a fundamental objectExpand
Spamming botnets: signatures and characteristics
TLDR
We developed a spam signature generation framework called AutoRE to detect botnet-based spam emails and botnet membership by leveraging both spam payload and spam server traffic properties. Expand
Heuristics for Vector Bin Packing
TLDR
We study heuristics for the Vector Bin Packing problem, where we are required to pack n items represented by d-dimensional vectors, into as few bins of size 1 each as possible. Expand
Achieving anonymity via clustering
TLDR
We propose a new method for anonymizing data records, where quasi-identifiers of data records are first clustered and then cluster centers are published. Expand
An Improved Construction for Counting Bloom Filters
TLDR
We provide a simple hashing-based alternative based on d- left hashing called a d-left CBF (dlCBF). Expand
Anonymizing Tables
TLDR
We consider the problem of releasing tables from a relational database containing personal records, while ensuring individual privacy and maintaining data integrity to the extent possible. Expand
Entropy based nearest neighbor search in high dimensions
  • R. Panigrahy
  • Mathematics, Computer Science
  • SODA '06
  • 6 October 2005
TLDR
In this paper we study the problem of finding the approximate nearest neighbor of a query point in the high dimensional space, focusing on the Euclidean space. Expand
Learning Polynomials with Neural Networks
TLDR
We study the effectiveness of learning low degree polynomials using neural networks by the gradient descent method. Expand
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