Abstract: Quantifying the privacy loss of a privacy-preserving mechanism on potentially sensitive data is a complex and well-researched topic; the de-facto standard for privacy measures are ε-differential privacy (DP) and its versatile relaxation (ε,δ)-approximate differential privacy (ADP). Recently, novel variants of (A)DP emerged and focused on giving tighter privacy bounds under continual observation.

Abstract: 

Oblivious computation refers to the ability to compute on "encrypted" data, such that neither intermediate results nor the program's runtime behavior reveal anything about secret inputs. Oblivious computation can enable privacy-preserving data mining for sensitive data (e.g., genomic or financial data), and allow businesses and individuals to monetize their data without compromising their privacy.

Abstract

Power and energy have become primary design constraints for modern processors. Power no longer scales with transistor channel length, causing power density to increase. Continued transistor scaling will require prohibitively expensive cooling technologies. This is particularly an issue in data centers, where the cost of power and cooling has a major impact on the total cost of ownership. Data centers have grown and expanded rapidly due to the advent of cloud computing and interactive web services.