Enhancing Privacy in Wearable IoT through a Provenance Architecture
Abstract
:1. Introduction
- Provenance is proposed to generate data trace routes in the wearable IoT economy order to ensure digital audit trail for transparency. The work adopted the broadcast-subscriber IoT architecture to ensure privacy of users’ data such as vitals especially in wearable IoT; a key concern in the generic broadcast IoT architecture.
- Both the hardware and software level data encoding methodologies are proposed based on device meta-data encryption.
2. Background Works
2.1. IoT Applications
2.2. Privacy and Provenance in IoT
2.3. The Open Issues
- How can the personal data shared in wearable IoT be made more secure when devices are broadcasting?
- How can the data be encoded in a way that even if the shared keys are stolen, data privacy will not be breached?
- Can transparency be offered to ensure the determination of who got what information in the wearable IoT?
3. The Designed IoT Architecture
3.1. Designing the System
3.2. Provenance and Audit Trail
- Step 1:
- Choose some random numbers , , ,, ,,, and compute
- Step 2:
- Choose the current time stamp and compute the challenge , where
- Step 3:
- Compute
- Step 4:
- Send the timestamp and to the middleware for authentication.
- Step 1:
- Compute ,, , and :
- Step 2:
- Check the following equation
- Step 1:
- The IoT device or the middleware first takes a version of the provenance record and sends the provenance information to an independent trusted authority, which is part of the middleware but a detached component.
- Step 2:
- The trusted authority, say uses its master key to compute and based on the outcome return to the middleware .
- Step 3:
- The middleware then looks up the tracking list with 𝐴. If an entry is found with , the IoT device which created the questionable version is tracked. The correctness is that, if the version is really generated by , we will have
4. Evaluation
4.1. The IoT Device Resource Utilization Cost
4.2. Sensor Data Propagation
4.3. Fault Injection Analysis
4.4. Visual Analytics from the Provenance Data
5. Conclusions
- Provenance is proposed to generate data trace routes in the wearable IoT economy order to ensure digital audit trail for transparency. The work adopted the broadcast-subscriber IoT architecture to ensure privacy of users’ data such as vitals especially in wearable IoT; a key concern in the generic broadcast IoT architecture.
- Both the hardware and software level data encoding methodologies are proposed based on device meta-data encryption.
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Property | Definition |
---|---|
Resource Utilization | The percentage of resources taken by the encryption schemes. |
Network Types | Different communication protocols such as ZigBee, HTTP, Bluetooth, and BLE. |
Encryption/Decryption Schemes | AES (aes_128_cfb), DES (des_cfb), 3DES. |
Hashing Schemes | MD5, and SHA (SHA-256). |
Soft-Real Time | Acceptable window within which data should be synchronized. |
Fault Injection | Attempts to hack the proposed broadcast-subscriber system for the purpose of testing. |
Resource | Utilization | |||
---|---|---|---|---|
Min (%) | Avg (%) | Max (%) | ||
CPU | AES | 6.75 | 11.15 | 16.75 |
DES | 10.44 | 16.51 | 20.44 | |
3DES | 11.89 | 17.10 | 23.96 | |
MD5 | 16.02 | 18.84 | 23.00 | |
SHA | 15.65 | 18.68 | 22.66 | |
RAM | AES | 4.25 | 5.66 | 8.96 |
DES | 5.23 | 7.82 | 10.95 | |
3DES | 5.87 | 9.00 | 12.44 | |
MD5 | 7.33 | 10.50 | 14.56 | |
SHA | 8.22 | 12.67 | 19.22 |
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Share and Cite
Lomotey, R.K.; Sofranko, K.; Orji, R. Enhancing Privacy in Wearable IoT through a Provenance Architecture. Multimodal Technol. Interact. 2018, 2, 18. https://doi.org/10.3390/mti2020018
Lomotey RK, Sofranko K, Orji R. Enhancing Privacy in Wearable IoT through a Provenance Architecture. Multimodal Technologies and Interaction. 2018; 2(2):18. https://doi.org/10.3390/mti2020018
Chicago/Turabian StyleLomotey, Richard K., Kenneth Sofranko, and Rita Orji. 2018. "Enhancing Privacy in Wearable IoT through a Provenance Architecture" Multimodal Technologies and Interaction 2, no. 2: 18. https://doi.org/10.3390/mti2020018