Protecting Privacy with Floating-Point Computation Toolkit

Protecting Privacy with Floating-Point Computation Toolkit

Table of Contents:

1. Introduction
   • Background Information
   • System Architecture and Definitions
2. Privacy-Preserving Routing Point Number Computation Protocols
   • Experiment and Analysis
3. Challenges in Secure Floating Point Calculations
   • Secure Integer Division Protocol
4. Proposed Privacy-Preserving Computation with Floating Point Numbers
   • Secure Floating Point Number Computation
5. Multi-User Support
   • Convenience and Cost-Effectiveness
6. Efficiency and Accuracy
   • Encrypted Floating Point Numbers
   • High Efficiency and Lower Accuracy Loss
7. System Architecture and Definitions
   • Crypto System
   • Semi-Honest Cloud Server
8. Proposed Protocols for Cloud Computation
   • Circular Floating Point Number Addition Protocol
   • Secure Multiplication and Exponent Calculation
   • Integral DJS Product Code
   • Signal Modular Calculation
   • Indicator Floating Division Protocol
9. Performance Evaluation and Comparison
   • Computation Cost and Joint Computing Time
   • Precision and Computation Time for Division
10. Conclusion and Limitations

Privacy-Preserving Computation using Floating Point Numbers

In recent years, as more and more enterprises and applications such as the Internet of Things and self-driving cars rely on data processing, the need for secure and efficient computation has become crucial. However, privacy concerns arise when multiple parties want to collaborate and analyze data while preserving data privacy. This paper aims to address the challenges of secure floating point calculations and proposes a privacy-preserving computation framework using floating point numbers.

Introduction

To provide a comprehensive understanding of this research, the first part of this paper provides background information on the topic. It explores the increasing reliance on data centers and the cloud for data processing, especially for computation outsourcing in cloud computing. The paper highlights the need for efficient and secure floating point calculations in computational outsourcing scenarios.

Privacy-Preserving Routing Point Number Computation Protocols

In this section, the paper introduces experimentation and analysis conducted to identify the challenges involved in secure floating point calculations. It explores existing secure integer division protocols and highlights the limitations of using homomorphic encryption for floating point computation. The paper emphasizes the need for improved efficiency and speed in secure floating point calculations.

Proposed Privacy-Preserving Computation with Floating Point Numbers

The Core contribution of this research lies in the proposal of a privacy-preserving computation framework that enables secure floating point number computations. The paper outlines the framework's ability to perform multiplication, addition, subtraction, and division operations on floating point numbers, building upon existing secure integer operations. It also presents Novel protocols for dividing floating point numbers.

Multi-User Support

This section explores the convenience and cost-effectiveness of the proposed framework in supporting multiple users. The paper discusses how the framework's design allows for the efficient distribution of secret keys among multiple participants. It emphasizes the importance of supporting multiple secret keys in computing and the benefits it brings to collaborative data analysis while ensuring privacy.

Efficiency and Accuracy

The proposed computation framework equips users with secure and efficient encrypted floating point numbers. The paper details the design and implementation of the framework, highlighting its high efficiency and lower accuracy loss compared to existing methods. The encryption scheme used ensures the privacy and integrity of floating point numbers, while still maintaining computation efficiency.

System Architecture and Definitions

To facilitate a better understanding of the proposed framework, this section introduces the system architecture and establishes the base definitions. The paper describes the crypto system, which involves the splitting of a private key into secret key 1 and secret key 2. It presents the system model and threat model, considering the entities involved, including the cloud server, computer service provider, multi-participant, and key generation center.

Proposed Protocols for Cloud Computation

This section delves into the protocols proposed for secure cloud computation. It presents protocols for circular floating point number addition, secure multiplication and exponent calculation, integral DJS product code computation, signal modular calculation, and indicator floating point division. The paper explains each protocol's working principles, highlighting the steps involved and the achieved privacy and security.

Performance Evaluation and Comparison

To assess the proposed framework's performance, the paper evaluates the computation cost and joint computing time compared to existing methods. It analyzes the precision and computation time for division, considering factors such as accuracy and efficiency. The paper presents experimental results and comparisons, showcasing the advantages of the proposed framework.

Conclusion and Limitations

In conclusion, this paper proposes a privacy-preserving computation framework that enables secure floating point number computations. The framework offers multiplication, addition, subtraction, and division operations while ensuring privacy, efficiency, and accuracy. However, it acknowledges limitations, such as the need for optimization in division communication time and the trade-off between division accuracy and efficiency.

Highlights:

• Proposed privacy-preserving computation framework for secure floating point number computations.
• Supports multiplication, addition, subtraction, and division operations on floating point numbers.
• Designed to ensure privacy, efficiency, and accuracy.
• Enables multiple users to collaborate while preserving data privacy.
• Evaluations Show improved computation cost, joint computing time, and precision compared to existing methods.

FAQ:

Q: What is the main contribution of this research? A: The main contribution of this research is the proposal of a privacy-preserving computation framework that enables secure floating point number computations.

Q: What operations are supported by the proposed framework? A: The proposed framework supports multiplication, addition, subtraction, and division operations on floating point numbers.

Q: How does the framework ensure privacy and data security? A: The framework employs encryption schemes to ensure the privacy and integrity of floating point numbers during computation.

Q: Can multiple users collaborate using this framework while preserving data privacy? A: Yes, the framework is designed to support multiple users and ensures convenience and cost-effectiveness in distributing secret keys for collaborative data analysis.

Q: How does the proposed framework compare to existing methods in terms of performance? A: Evaluations demonstrate improved computation cost, joint computing time, precision, and overall efficiency compared to existing methods.