Data Security in Internet of Things

Overview & Implementation

This case study highlights the research assistance provided by TEQ Research Solution for a Ph.D. research project titled “Data Security in Internet of Things” in the domain of IoT Security, Encrypted Databases, and Secure Query Processing. The research focused on developing a secure encrypted query processing framework named IoTCryptDB to enhance data confidentiality, secure query execution, and privacy protection in Internet of Things (IoT) environments.

Problem Statement

IoT applications continuously generate and store sensitive data in cloud and distributed environments. Existing encrypted database systems such as:

• CryptDB

• MONOMI

• SDB

• TrustedDB

• Cipherbase

faced several limitations including:

• Limited query support

• High execution overhead

• Poor analytical query handling

• High latency

• Resource constraints in IoT devices

• Weak support for secure aggregation and sub-queries

The implementation of heavy cryptographic schemes on IoT devices also caused challenges related to CPU, memory, bandwidth, and energy consumption.

Proposed Solution

TEQ Research Solution assisted in developing an advanced encrypted database architecture called IoTCryptDB.

The proposed system provided:

• Secure encrypted query processing

• Strong data confidentiality

• Efficient analytical query execution

• Secure cloud database integration

• Access control and authentication

• Improved query response performance

The architecture integrated advanced encryption mechanisms such as:

• Elliptic Curve Cryptography (ECC)

• Hash Encryption

• Aggregation Encryption

• Analytical Encryption

• Sub Query Encryption

• Homomorphic Encryption

The solution enabled efficient encrypted SQL query execution without compromising security performance.

Technologies & Research Areas

• Internet of Things (IoT)

• Cloud Security

• CryptDB

• Encrypted Query Processing

• Secure Databases

• Homomorphic Encryption

• Apache Hadoop

• Spark

• Hive

• C++ with GMP Library

Experimental Analysis

The proposed IoTCryptDB framework was experimentally compared with existing encrypted database systems including:

• CryptDB

• MONOMI

• SDB

• TrustedDB

Performance Metrics Evaluated

• Execution Time

• Throughput

• Query Response Time

• Bandwidth Efficiency

• Latency

• Overall Cost

• Query Selectivity

Experimental Environment

The implementation was tested using:

• Ubuntu 12.04

• Intel i7 Processors

• Hadoop 2.4.1

• Spark 1.1.0

• Hive 0.12.0

TPC-H benchmark queries were used for evaluating encrypted query performance across multiple scenarios.

Key Findings

The proposed IoTCryptDB achieved:

• Lower query execution time

• Better throughput performance

• Reduced processing overhead

• Faster encrypted query execution

• Improved analytical query support

• Enhanced security and privacy protection

• Better performance than CryptDB, MONOMI, and SDB

The system successfully demonstrated efficient encrypted database processing for IoT applications with strong security guarantees.

Research Contributions

The research produced several academic outcomes including:

International Journal Publications

• Study on Security in Internet of Things

• Review on IoT Security

• Processing Encrypted Query Data in IoT

• CryptDB and TrustedDB Analysis

• Secure Query Processing Research

• Encrypted Database Performance Studies

• IoT Data Security Frameworks

Academic Contributions

• IoT Security Research

• Secure Database Architecture

• Encrypted Query Optimization

• Performance Evaluation using TPC-H Benchmark

TEQ Research Solution Contribution

TEQ Research Solution provided complete research assistance including:

• Research problem formulation

• Literature survey support

• IoT security framework guidance

• Experimental setup assistance

• Performance analysis

• Benchmark evaluation

• Synopsis and thesis preparation

• Journal publication support

Outcome

The proposed IoTCryptDB framework successfully enhanced data security and encrypted query processing performance in IoT environments. The research demonstrated that secure encrypted databases can efficiently support complex analytical queries while maintaining strong confidentiality and privacy protection.

Worked For

G. Ambika – Research Scholar

Achievement

We had assisted for 7 papers in International Journals.