Multi Party Computation Motivated by the Birthday Problem

Suppose there are n people in a classroom and we want to decide if there are two of them who were born on the same day of the year. The well-known birthday paradox is concerned with the probability of this event and is discussed in many textbooks on probability. In this paper we focus on cryptographic aspects of the problem: how can we decide if there is a collision of birthdays without the participants disclosing their respective date of birth. We propose several procedures for solving this in a privacy-preserving way and compare them according to their computational and communication complexity.

Download Full-text

Exploring the Birthday Paradox Using a Monte Carlo Simulation and Graphing Calculators

Mathematics Teacher ◽

10.5951/mt.94.4.0258 ◽

2001 ◽

Vol 94 (4) ◽

pp. 258-262

Author(s):

Matthew C. Whitney

Keyword(s):

Monte Carlo Simulation ◽

Monte Carlo ◽

Graphing Calculators ◽

Graphing Calculator ◽

Student Interest ◽

Birthday Problem ◽

Birthday Paradox

A lesson using a graphing calculator and the “birthday problem” to stimulate student interest in probability.

Download Full-text

Privacy-Aware MapReduce Based Multi-Party Secure Skyline Computation

Information ◽

10.3390/info10060207 ◽

2019 ◽

Vol 10 (6) ◽

pp. 207

Author(s):

Saleh Ahmed ◽

Mahboob Qaosar ◽

Asif Zaman ◽

Md. Anisuzzaman Siddique ◽

Chen Li ◽

...

Keyword(s):

Data Privacy ◽

Large Scale ◽

Communication Complexity ◽

Cost Effective ◽

Privacy Preserving ◽

Secure Computation ◽

Security And Privacy ◽

Computation Method ◽

Computation Model ◽

Privacy And Security

Selecting representative objects from a large-scale dataset is an important task for understanding the dataset. Skyline is a popular technique for selecting representative objects from a large dataset. It is obvious that the skyline computation from the collective databases of multiple organizations is more effective than the skyline computed from a database of a single organization. However, due to privacy-awareness, every organization is also concerned about the security and privacy of their data. In this regards, we propose an efficient multi-party secure skyline computation method that computes the skyline on encrypted data and preserves the confidentiality of each party’s database objects. Although several distributed skyline computing methods have been proposed, very few of them consider the data privacy and security issues. However, privacy-preserving multi-party skyline computing techniques are not efficient enough. In our proposed method, we present a secure computation model that is more efficient in comparison with existing privacy-preserving multi-party skyline computation models in terms of computation and communication complexity. In our computation model, we also introduce MapReduce as a distributive, scalable, open-source, cost-effective, and reliable framework to handle multi-party data efficiently.

Download Full-text