The Internet of Things (IoT) harbors a large number of resource-limited devices (e.g., sensors) that continuously
generate and offload sensitive information (e.g., financial, health, personal). It is imperative the ensure the trustworthiness of this data with efficient cryptographic mechanisms. Digital signatures can offer scalable authentication with public verifiability and nonrepudiation. However, the state-of-the-art digital signatures do not offer the desired efficiency and are not scalable for the connected resource-limited IoT devices. This is without considering long term security features such as post-quantum security and forward security. In this paper, we summarize the main challenges to an energy-aware and efficient signature scheme. Then, we propose
new scheme design improvements that uniquely embed different emerging technologies such as Mutli-Party Computation (MPC) and secure enclaves (e.g., Intel SGX) in order to secret-share confidential keys of low-end IoT devices across multiple cloud servers. We also envision building signature schemes with Fully Homomorphic Encryption (FHE) to enable verifiers to compute expensive commitments under encryption. We provide evaluation metrics that showcase the feasibility and efficiency of our designs for potential deployment on embedded devices in IoT.
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Security Analysis in Context-Aware Distributed Storage and Query Processing in Hybrid Cloud Framework
Recent studies have shown that several government and business organizations experience huge data breaches. Data breaches increase in a daily basis. The main target for attackers is organization sensitive data which includes personal identifiable information (PII) such as social security number (SSN), date of birth (DOB) and credit card /debit card (CCDC). The other target is encryption/decryption keys or passwords to get access to the sensitive data. The cloud computing is emerging as a solution to store, transfer and process the data in a distributed location over the Internet. Big data and internet of things (IoT) increased the possibility of sensitive data exposure. Most methods used for the attack are hacking, unauthorized access, insider theft and false data injection on the move. Most of the attacks happen during three different states of data life cycle such as data-at-rest, data-in-use, and data-in-transit. Hence, protecting sensitive data at all states particularly when data is moving to cloud computing environment needs special attention. The main purpose of this research is to analyze risks caused by data breaches, personal and organizational weaknesses to protect sensitive data and privacy. The paper discusses methods such as data classification and data encryption at different states to protect personal and organizational sensitive data. The paper also presents mathematical analysis by leveraging the concept of birthday paradox to demonstrate the encryption key attack. The analysis result shows that the use of same keys to encrypt sensitive data at different data states make the sensitive data less secure than using different keys. Our results show that to improve the security of sensitive data and to reduce the data breaches, different keys should be used in different states of the data life cycle.
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- Award ID(s):
- 1828811
- PAR ID:
- 10094383
- Date Published:
- Journal Name:
- 2019 IEEE 9th Annual Computing and Communication Workshop and Conference (CCWC)
- Page Range / eLocation ID:
- 0177 to 0183
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/CCWC.2019.8666498
