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##  Appendix A Additional Figures and Comparison Tables

Data provenance is presented in many domains such as file systems, databases,
cloud computing, IoT, distributed networks and many more domains. This
research focuses on data provenance within the IoT and WSN domains, as shown
in Figure 6.

Figure 6. Provenance domains and the scope of our research.

Internet of Things (IoT) networks consist of a number of sensing nodes that
communicate using a wireless multi-hop model, as shown in Figure 7.

Figure 7. IoT network model assumed in our work.

Figure 8 provides a taxonomy of attacks against IoT networks.

Figure 8. Taxonomy of security attacks in IoT networks with data provenance
approach. Table 4. Comparative analysis of selected studies.

Reference | Year |  Provenance Encoding | Provenance Storage |  Application | Security |  Pros |  Cons
---|---|---|---|---|---|---|---
Method | Method |  | Analysis
Cryptography-based
Lim et al. (2010) |  (2010) |  Trust scores | N/A |  Sensor Network | ✗ |  Practical solution for trustworthiness assessment. |  The method is based on the principle that the more trustworthy data a source provides, the more trusted the source is considered. Many attacks can deceive the system and overcome the trustworthiness of data.
Wang et al. (2016a) |  (2016a) |  Path Index and MAC | Distributed database |  WSN |  1F5F8 |  Embeds path index instead of data path, which reduces the size of the embedded information in each packet. Each packet path is stored at the forwarding nodes in the network. |  Provenance information is only accessible at the BS and cannot be verified at each stage of the path, provenance data is not securely transmitted and stored.
Chia et al. (2017) |  (2017) |  Shamir secret sharing and threshold cryptography | Smart meter |  Home Energy Monitoring Networks | ✗ |  Achieves source identity authenticity, location authenticity, data consistency and source data authenticity. |  The proposed solution does not consider the storage of provenance information, multi-hop architecture and provenance encoding.
Suhail et al. (2018) |  (2018) |  Hash chain | In-packet |  Sensor network | ✗ |  Keeps track of data packets using a chain of provenance records that store that hash of traversed node ID. |  Integrity is verified at the destination. Provenance size grows very fast as the number of forwarding nodes increase, link overhead due size increase in forwarded packets.
Xu et al. (2019) |  (2019) |  Path index differences | Network nodes |  WSN | ✗ |  High provenance compression rate. Whenever the number of hops increases the provenance size nearly remains at a constant level. |  Provenance information is based on a few number of data features. Dictionaries at each node increase in size as the network scale and the data packets increase.
Suhail et al. (2020) |  (2020) |  In-packet embedding | Routing table |  RPL-based IoT network |  1F5F8 |  Constant provenance size, used energy consumption, enhanced provenance generation time. |  Considers robustness against only three attacks. Provenance information is node ID and sequence number.
Liu and Wu (2020) |  (2020) |  Common substring matching | Distributed database |  Multihop IoT network | ✗ |  Malicious node identification. High provenance decoding accuracy. Stable provenance length after all the path have been traversed |  Many provenance fields that increase the size compared to other techniques. Lack of security analysis against different types of attacks.
Tang and Keoh (2020) |  (2020) |  MAC | in-packet |  HAN with smart metering |  1F5F8 |  Use of a symmetric key approach to improve efficiency over asymmetric key-based approaches. |  Needs to integrate a third sender to the system to achieve location verification. Solution for single-hop data transfer scenario only.
Porkodi and Kesavaraja (2021) |  (2021) |  Hash function and symmetric encryption | N/A |  IoT network |  1F5F8 |  Multiple levels of authorities. Lightweight key management. |  Verifying data origin is not satisfied. Needs for provenance storage. Provenance information is not sufficient to establish trustworthiness in the system.
Xu et al. (2022) |  (2022) |  Path index and Packet path hash value | Distributed node database |  Zigbee WSN | ✗ |  Reduces the negative impact of network topology changes. Maintains high provenance compression and keeps the provenance size constant |  Increase in provenance size as network becomes larger in the PIDP scheme. Lacks analysis to different security threats.
Xu and Wang (2022) |  (2022) |  Dictionary-based provenance scheme/ Hash functions | Network nodes |  ZigBee sensor network | ✗ |  WSN topology graph is presented as a series of different granularity topology graphs. Encoding provenance on high granularity levels which skips provenance updating at some nodes. Decrease in the provenance updating latency. |  The scheme yields high cost in terms of computation and storage in sensor nodes, which are resource constrained and require lightweight schemes.
Bloom Filters
Sultana et al. (2015) |  (2015) |  in-packet Bloom filters | in-packet chain |  WSN |  1F5F8 |  Simple encoding scheme. Reduces the size of provenance length. Scalable for a high number of nodes. |  Provenance information is not sufficient (only node ID). Sends the complete data path within the packet.
Siddiqui et al. (2019) |  (2019) |  Bloom Filters with cryptographic mechanisms | in-packet |  N/A | ✗ |  The computation of the partial digital signature is carried out by the IoT node, while the more resource-intensive calculations are offloaded and performed by the edge node. Enhanced storage capabilities. |  Fast increment of provenance size. Complete provenance is appended to the data packet.
Harshan et al. (2020) |  (2020) |  Bloom Filter | In-packet |  Raspberry Pi network | ✗ |  At most half the nodes in the path modify the provenance. Reduction in the delay on packets. |  Storage overhead in large-scale networks as the number of hops increases.
Fingerprints
Ali et al. (2014) |  (2014) |  Link Fingerprints | Device Database |  Bodyworn Sensors | ✗ |  Use of quantization to distill the RSSI data into a much smaller size to overcome storage challenges in memory constrained sensor devices. |  Considers only single-hop scenarios. Data provenance operates on a per-session basis and is not validated for each packet sent.
Alam and Fahmy (2014) |  (2014) |  Prime multiplication and Rabin Fingerprints | In-packet buffer |  IoT sensor network with TelosB motes | ✗ |  The use of small number of bits to encode higher number of node IDs and requires fewer packets to construct network-wide provenance. |  Provenance information is based on only node IDs, which is not sufficient to encounter many attacks.
Kamal and Tariq (2018) |  (2018) |  Link Fingerprint | in-packet and server |  Multihop IoT network usinf MICAz motes | ✗ |  The provenance relies on the next hop node using the RSSI, which is related to any secrets stored in the nodes. |  The higher the value of correlation coefficient, the higher the percentage of the secured data transfers that can be deceived by an attacker when compromising a group of colliding nodes.
Blockchain-based
Baracaldo et al. (2017) |  (2017) |  Keyless Signature Infrastructure Module (KSI) | Blockchain |  N/A | ✗ |  Provenance data that is secured can only be accessed by authorized users. Lightweight and scalable architecture for IoT applications. |  Data points generated from IoT devices/sensors are not secured when communicated to the gateway and then to the policy engine and KSI before storing the provenance data as a transaction in the blockchain.
Zeng et al. (2018) |  (2018) |  Ethereum blockchain using edge computing nodes | blockchain database |  Raspberry Pi Nodes and micaz motes | ✗ |  No provenance compression is needed. Secure provenance storage through blockchain database. |  Each data packet requires a transaction for updating provenance information. Large number of generated data packets from sensor nodes, requiring a complex and costly method to store and query each provenance record for the data packets from the blockchain.
Javaid et al. (2018) |  (2018) |  PUFs | Blockchain |  Linux working environment |  1F5F8 |  Prone to single point of failure due to the decentralized architecture. Smart contracts enable a safe and secure mechanism for the transmission, authentication and storage of requests. |  Each PUF Challenge Response Pair (CRP) and the address of IoT device is stored by the smart contract. The huge number of data generated makes it complex to store this amount of data using a blockchain.
Sigwart et al. (2020) |  (2020) |  Data points | Blockchain-based |  General |  1F5F8 |  A general framework for different IoT applications. Layered architecture of smart contracts. |  No consideration for the secure transmission of provenance records. New architecture is needed in the IoT platform.

Reference | Year |  Provenance Encoding | Provenance Storage |  Application | Security |  Pros |  Cons
---|---|---|---|---|---|---|---
Method | Method |  | Analysis
Blockchain-based
Liu et al. (2020) |  (2020) |  Hash function and K-Hop Ancestor | Blockchain |  IoT application with blockchain network |  1F5F8 |  Examines existing security concerns in the distributed IoT network architecture. Uses blockchain as the fundamental architecture for storing and retrieving cross-domain provenance data by using its decentralization and immutability. |  Querying provenance information needs to be optimized due to the cost of retrieving on/off blockchain storage.
Siddiqui et al. (2020) |  (2020) |  Ciphertext-Policy Attribute based Encryption (CP-ABE) | Blockchain and centralized database |  Cloud based IoT | ✗ |  By applying partial signatures, it is possible to offload the blockchain method and associated overhead from the IoT node to the edge nodes. |  Each provenance record must be stored twice (requiring additional storage and communication overhead), with the block being saved in the provenance database and the provenance data being published on the blockchain network by a provenance auditor.
Yin and Fu (2022) |  (2022) |  Smart contract | Blockchain |  Ethereum network | ✗ |  Users can not access provenance information without access permission from data owner. Each time an operation is performed the authority is checked. |  Provenance information needs to be communicated securely with the blockchain storage. Each operation need to be set as a transaction to be stored within the blockchain, which makes it complex.
Sun et al. (2022) |  (2022) |  Hash function and Homomorphic Signature | Blockchain |  Multi-layer IoT applications | ✗ |  Detects abnormal data operations using the provenance graph with integrity verification using a signature and a hash function on each IoT node. |  Provenance information is not securely communicated with the edge node or gateway.
Chenli et al. (2022) |  (2022) |  Directed graph and Chameleon Hash | Blockchain |  Decentralized data sharing applications |  1F5F8 |  Performs both forward and backward tracking. Uses networked blockchain instead of single blockchain for storing provenance records. |  Costly in terms of computation and storage for resource limited devices such as sensors and devices in IoT networks.
Watermarking
Sultana et al. (2011) |  (2011) |  Watermarking based | inter packet delays |  sensor network | ✗ |  High detection accuracy, energy efficiency |  Drastic increase in provenance size as number of nodes increases
Physical Unclonable Functions
Aman et al. (2019) |  (2019) |  PUFs and RSSI | Device memory and server |  Indoor laboratory IoT environment |  1F5F8 |  Unclonability and robustness against physical attacks through avoiding the need to store secret keys. |  Provenance information is based only on device’s pseudonym identity and RSSI, which is not enough to obtain any attack attempt in packet drop, replay, and modification.
Aman et al. (2021) |  (2021) |  PUFs, wireless fingerprints | server database |  IoT network with MICA-Z motes |  1F5F8 |  IoT devices do not store secrets in their memory. Privacy preservation. Resilience against Ephemeral Secret Leakage (ESL) attacks. |  Requires computation of many session keys for each node. Lacks security analysis against different type of attacks.
Hamadeh and Tyagi (2021) |  (2021) |  PUFs | Network nodes and server |  FPGA Altera Cyclone | ✗ |  Source identity authenticity through PUF. IoT node is able to send anonymously data to the server. |  The system does not consider the traceability of data packets along the data path. Multi-hop model with the presence of forwarding devices is not considered. Selecting and computing a secret key in each round results in computational overhead.
Graph-based
Chang et al. (2022) |  (2022) |  Event-flow graphs | Provenance server |  IoT smart apps | ✗ |  Observes provenance without utilizing a sophisticated query language by just selecting the appropriate nodes on an event flow graph. |  As the number of events and actions increases, the provenance data becomes large in scale over time, provenance data is not securely stored in the provenance server.
Graph-based
Jaigirdar et al. (2023) |  (2023) |  Provenance graphs | Cloud |  IoT Health applications | ✗ |  The model includes security-aware properties at every step of data transmission. Provides the status of each device as data processing mechanism, software running and communication channels properties |  Single point of failure where provenance information is stored in the cloud. All the devices/sensors and users need to forward and retrieve this information from it, including auditor, doctor, user, and gateway.
Al-Rakhami and Al-Mashari (2022) |  (2022) |  Directed Acyclic Graph | Cloud server |  Industrial Internet of Things (IIoT) | ✗ |  Scalability, affordability, and quantum robustness are all associated with the adoption of IOTA’s distributed ledger technology (DLT). |  MQTT protocol is used to store and manage the majority of the data that is gathered by our system, but because it does not execute or impose data encryption, it is not completely safe against tampering.
|  |  |  |  |  |  |
Data Sanization
Lomotey et al. (2019) |  (2019) |  Data sanitization | Local database |  Sensor network | ✗ |  Hides sensitive data that users do not want to share with others by tagging attributes in the provenance information. |  Provenance information is not secure. Storing and querying provenance information is not defined. Integrity is not ensured.
Lexial Chaining
Lomotey et al. (2018) |  (2018) |  Associative rules and statistical lexical chaining | Centralized database (CouchDB) |  Devices with Bluetooth in a machine-to-machine (M2M) scenario | ✗ |  During machine-to-machine communication, it is possible to track and categorize the communication routes taken by various IoT devices as well as their previous connections. |  Lexical chains are not securely communicated between the different entities and the database.
Path Difference
Gao et al. (2013) |  (2013) |  Path difference and speculation | In-packet/ PC database |  WSN | ✗ |  Lightweight approach that does not require complex computation at sensor nodes. In case of inability to record path difference, a path speculation method can reconstruct the routing path. |  The path field container is limited to a number of bits that cannot be exceeded. When the path difference is large and exceeds this limit, the path cannot be recorded completely.
Logging-based
Sadineni et al. (2023) |  (2023) |  Provenance logs and network traffic | Centralized database |  Link-Layer Forensics in IoT | ✗ |  Detects network and link layer attacks in IoT networks. Detects stealthy attacks. |  Provenance information is not securely transmitted through transmission channel. The stored provenance information can be altered. Multi-hop provenance path construction is not studied. Due to rapid increase in provenance information, there is a storage overhead at the database.
Frameworks using storing methods
Alkhalil et al. (2019) |  (2019) |  Trust model based on fuzzy logic | Network Nodes |  WSN | ✗ |  Node’s trust is based on availability, neighboring nodes evaluation, and message drop rate. |  The study needs to consider security issues in-terms of integrity and secure transmission of data. Is not clear how provenance information is encoded and stored.
Wang et al. (2018) |  (2018) |  Provenance as sources and sinks | Centralized database |  Samsung SmartThings IoT Platform | ✗ |  Complete platform for provenance tracking in IoT applications. Records provenance information by a provenance collector directly from IoT devices. |  No security mechanism is applied to secure the transmission of provenance information. Multi-hop tracking is not considered.
Elkhodr and Alsinglawi (2020) |  (2020) |  Store device status | Local database |  IoT Management Platform | ✗ |  Stores the provenance information as device status in a centralized database including the visiting and returning devices. |  Provenance information is not secured. Multi-hop model is not taken into consideration. Security requirements in terms of data and provenance are not studied.

Table 5. Provenance category and evaluation methodology used in the selected security techniques. Reference |  Provenance Category |  Implementation |  Simulation
---|---|---|---
Lim et al. (2010) |  Cryptography-based |  ✗ |  Java
Wang et al. (2016a) |  Cryptography-based |  TelosB motes |  TinyOS 2.1.2 TOSSIM
Chia et al. (2017) |  Cryptography-based |  Raspberry Pi 3 and DLink DSP-W215 Wi-Fi Smart Plug |  Java
Suhail et al. (2018) |  Cryptography-based |  ✗ |  Contiki simulator, Cooja (Osterlind et al., 2006)
Xu et al. (2019) |  Cryptography-based |  Testbed with Zigbee sensor nodes |  TinyOS 2.1.2/TOSSIM, PowerTOSSIMz
Suhail et al. (2020) |  Cryptography-based |  ✗ |  Contiki simulator, Cooja
Liu and Wu (2020) |  Cryptography-based |  ✗ |  Python
Tang and Keoh (2020) |  Cryptography-based |  ✗ |  ✗
Porkodi and Kesavaraja (2021) |  Cryptography-based |  ✗ |  N/A
Xu et al. (2022) |  Cryptography-based |  Zigbee sensor nodes with TI CC2530 microcontroller |  TOSSIM/TinyOS 2.1.2 and PowerTOSSIMz (Perla et al., 2008)
Xu and Wang (2022) |  Cryptography-based |  ZigBee sensor/TI CC2530 microcontroller |  TinyOS 2.1.2/TOSSIM, PowerTOSSIMz
Sultana et al. (2015) |  Bloom Filter |  ✗ |  TinyOS simulator
Siddiqui et al. (2019) |  Bloom Filter |  ✗ |  Java
Harshan et al. (2020) |  Bloom Filters |  Raspberry Pi 3+ and a Digi XBee S2C |  ✗
Ali et al. (2014) |  Link Fingerprints |  MICAz motes, running TinyOS |  ✗
Alam and Fahmy (2014) |  Rabin Fingerprints |  TelosB motes |  TOSSIM
Kamal and Tariq (2018) |  Link Fingerprints |  MICAz motes |  ✗
Baracaldo et al. (2017) |  Blockchain-based |  ✗ |  ✗
Zeng et al. (2018) |  Blockchain-based |  MICAz motes |  TOSSIM/TinyOS 2.1.2
Javaid et al. (2018) |  PUFs and Blockchain |  ✗ |  Solidity IDE, Remix
Sigwart et al. (2020) |  Blockchain-based |  ✗ |  Ethereum networks Rinkeby and Ropsten
Liu et al. (2020) |  Blockchain-based |  Python interface/Pinocchio (Parno et al., 2016) and libsnark interface (Kosba et al., 2018) with Etheruem test network |  ✗
Siddiqui et al. (2020) |  Blockchain-based |  ✗ |  Hyperledger Fabric (Cachin, 2016)/ Byzantine fault-tolerant state machine replication library - Java (BFT-SMaRt) (Sousa et al., 2018)
Yin and Fu (2022) |  Blockchain-based |  ✗ |  Ethereum network test/ Ubuntu
Sun et al. (2022) |  Blockchain-based |  ✗ |  Public IoT database MMASH (Rossi et al., 2020)
Chenli et al. (2022) |  Blockchain-based |  ✗ |  Go language and Hyperledger Fabric v2.0
Sultana et al. (2011) |  Watermarking |  ✗ |  TinyOS simulator
Aman et al. (2019) |  PUFs and Link Fingerprints |  MICAz motes/ MATLAB |  MICA 2 mote platform/ AVRORA
Aman et al. (2021) |  PUFs |  MICAz motes, CC2420 transceiver |  ✗
Hamadeh and Tyagi (2021) |  PUFs |  FPGA Altera Cyclon |  Verilog (HDL), ModelSim XE
Chang et al. (2022) |  Event-flow graphs |  ✗ |  Gentle Wake-Up, and Smart Security apps using SmartThings IDE (SmartThings, 2023; Co., 2023)
Jaigirdar et al. (2023) |  Provenance graphs |  ✗ |  Flask module in python/ Neo4j
Al-Rakhami and Al-Mashari (2022) |  Directed Acyclic Graph |  ✗ |  Eclipse Paho 222https://www.eclipse.org/paho/ and Python
Lomotey et al. (2019) |  Data Sanitization |  ✗ |  ✗
Lomotey et al. (2018) |  Lexical chaining techniques |  Bluetooth devices/Amazon EC2 platform |  ✗
Gao et al. (2013) |  Path difference |  ✗ |  TinyOS 2.1.1 with CTP (Gnawali et al., 2009)/ TOSSIM
Sadineni et al. (2023) |  Logging-based |  ✗ |  Contiki simulator, Cooja
Alkhalil et al. (2019) |  Fuzzy logic |  ✗ |  Java program
Wang et al. (2018) |  Centralized Database |  ✗ |  Samsung SmartThings IDE (Samsung, 2017)
Elkhodr and Alsinglawi (2020) |  Management platform |  ✗ |  ✗
Table 6. Performance metrics used in the selected studies. Reference | Prov. | Energy | Data Packet | Link-Loss | Detection | False | False | Computation
---|---|---|---|---|---|---|---|---
Length | Consumption | Size | Rate | Rate | Positive Rate | Negative Rate | time
Lim et al. (2010) | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |  1F5F8
Wang et al. (2016a) |  1F5F8 |  1F5F8 | ✗ |  1F5F8 |  1F5F8 | ✗ | ✗ |  1F5F8
Chia et al. (2017) | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |  1F5F8
Suhail et al. (2018) | ✗ |  1F5F8 | ✗ | ✗ | ✗ | ✗ | ✗ | ✗
Xu et al. (2019) |  1F5F8 |  1F5F8 |  1F5F8 | ✗ | ✗ | ✗ | ✗ | ✗
Suhail et al. (2020) |  1F5F8 |  1F5F8 | ✗ | ✗ |  1F5F8 | ✗ | ✗ |  1F5F8
Liu and Wu (2020) |  1F5F8 | ✗ | ✗ | ✗ |  1F5F8 |  1F5F8 |  1F5F8 | ✗
Tang and Keoh (2020) | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗
Porkodi and Kesavaraja (2021) | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |  1F5F8
Xu et al. (2022) |  1F5F8 |  1F5F8 |  1F5F8 | ✗ | ✗ |  1F5F8 | ✗ | ✗
XXu and Wang (Xu and Wang, 2022) |  1F5F8 |  1F5F8 |  1F5F8 | ✗ |  1F5F8 | ✗ | ✗ |  1F5F8
Sultana et al. (2015) |  1F5F8 |  1F5F8 | ✗ |  1F5F8 |  1F5F8 |  1F5F8 | ✗ | ✗
Siddiqui et al. (2019) | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |  1F5F8
Harshan et al. (2020) |  1F5F8 | ✗ | ✗ | ✗ |  1F5F8 | ✗ | ✗ |  1F5F8
Ali et al. (2014) | ✗ |  1F5F8 |  1F5F8 | ✗ | ✗ | ✗ | ✗ |  1F5F8
Alam and Fahmy (2014) |  1F5F8 |  1F5F8 |  1F5F8 | ✗ | ✗ | ✗ | ✗ | ✗
Kamal and Tariq (2018) | ✗ |  1F5F8 | ✗ | ✗ | ✗ | ✗ | ✗ |  1F5F8
Baracaldo et al. (2017) | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗
Zeng et al. (2018) |  1F5F8 | ✗ |  1F5F8 | ✗ | ✗ | ✗ | ✗ | ✗
Javaid et al. (2018) | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗
Sigwart et al. (2020) | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |  1F5F8
Liu et al. (2020) |  1F5F8 | ✗ |  1F5F8 | ✗ | ✗ | ✗ | ✗ |  1F5F8
Siddiqui et al. (2020) | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |  1F5F8
Yin and Fu (2022) | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |  1F5F8
Sun et al. (2022) | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |  1F5F8
Chenli et al. (2022) | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |  1F5F8
Sultana et al. (2011) | ✗ | ✗ |  1F5F8 | ✗ |  1F5F8 |  1F5F8 |  1F5F8 | ✗
Aman et al. (2019) |  1F5F8 |  1F5F8 |  1F5F8 | ✗ | ✗ | ✗ | ✗ |  1F5F8
Aman et al. (2021) |  1F5F8 |  1F5F8 | ✗ | ✗ | ✗ | ✗ | ✗ | ✗
Hamadeh and Tyagi (2021) | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |  1F5F8
Chang et al. (2022) | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |  1F5F8
Jaigirdar et al. (2023) |  1F5F8 | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |  1F5F8
Al-Rakhami and Al-Mashari (2022) | ✗ | ✗ |  1F5F8 | ✗ | ✗ | ✗ | ✗ |  1F5F8
Lomotey et al. (2019) | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗
Lomotey et al. (2018) | ✗ | ✗ | ✗ | ✗ |  1F5F8 |  1F5F8 |  1F5F8 |  1F5F8
Gao et al. (2013) |  1F5F8 | ✗ |  1F5F8 |  1F5F8 | ✗ |  1F5F8 | ✗ |  1F5F8
Sadineni et al. (2023) |  1F5F8 | ✗ |  1F5F8 | ✗ | ✗ | ✗ | ✗ | ✗
Alkhalil et al. (2019) | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |  1F5F8
Wang et al. (2018) |  1F5F8 | ✗ |  1F5F8 |  1F5F8 | ✗ | ✗ | ✗ |  1F5F8
Elkhodr and Alsinglawi (2020) | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗
Table 7. Security requirements in the selected studies. Reference | Security Requirements
---|---
Data Integrity | Confidentiality | Availability | Privacy | Freshness | Non-repudiation | Unforgeability
Lim et al. (2010) | N/A | N/A | YES | N/A | YES | N/A | N/A
Wang et al. (2016a) | YES | YES | YES | N/A | YES | YES | N/A
Chia et al. (2017) | YES | N/A | N/A | N/A | N/A | YES | YES
Suhail et al. (2018) | YES | N/A | N/A | N/A | YES | YES | YES
Xu et al. (2019) | YES | N/A | N/A | N/A | N/A | YES | N/A
Suhail et al. (2020) | N/A | N/A | YES | N/A | YES | YES | N/A
Liu and Wu (2020) | YES | N/A | YES | N/A | YES | N/A | YES
Tang and Keoh (2020) | YES | YES | N/A | N/A | N/A | N/A | YES
Porkodi and Kesavaraja (2021) | YES | YES | YES | N/A | N/A | N/A | YES
Xu et al. (2022) | YES | YES | YES | N/A | N/A | YES | YES
Xu and Wang (2022) | YES | N/A | YES | N/A | N/A | YES | YES
Sultana et al. (2015) | YES | YES | YES | N/A | YES | N/A | N/A
Siddiqui et al. (2019) | YES | YES | YES | N/A | N/A | YES | N/A
Harshan et al. (2020) | YES | N/A | YES | N/A | N/A | YES | YES
Ali et al. (2014) | YES | YES | N/A | N/A | YES | YES | YES
Alam and Fahmy (2014) | N/A | N/A | N/A | N/A | N/A | N/A | N/A
Kamal and Tariq (2018) | YES | N/A | YES | N/A | YES | N/A | N/A
Baracaldo et al. (2017) | YES | YES | N/A | YES | N/A | YES | N/A
Zeng et al. (2018) | YES | YES | N/A | N/A | YES | YES | YES
Javaid et al. (2018) | YES | N/A | N/A | N/A | YES | YES | N/A
Sigwart et al. (2020) | YES | N/A | YES | N/A | N/A | YES | N/A
Liu et al. (2020) | YES | N/A | N/A | N/A | YES | YES | YES
Siddiqui et al. (2020) | YES | YES | N/A | N/A | YES | YES | N/A
Yin and Fu (2022) | YES | YES | N/A | YES | N/A | YES | N/A
Sun et al. (2022) | YES | YES | N/A | N/A | N/A | N/A | YES
Chenli et al. (2022) | YES | YES | N/A | N/A | N/A | N/A | N/A
Sultana et al. (2011) | YES | YES | YES | N/A | YES | N/A | N/A
Aman et al. (2019) | YES | YES | N/A | YES | YES | YES | N/A
Aman et al. (2021) | YES | N/A | N/A | N/A | YES | N/A | N/A
Hamadeh and Tyagi (2021) | YES | N/A | YES | YES | YES | YES | N/A
Chang et al. (2022) | N/A | N/A | N/A | N/A | YES | N/A | N/A
Jaigirdar et al. (2023) | YES | N/A | YES | N/A | N/A | YES | YES
Al-Rakhami and Al-Mashari (2022) | YES | YES | YES | N/A | N/A | N/A | YES
Lomotey et al. (2019) | N/A | YES | N/A | YES | N/A | YES | N/A
Lomotey et al. (2018) | YES | YES | YES | N/A | N/A | YES | N/A
Gao et al. (2013) | N/A | N/A | N/A | N/A | YES | YES | N/A
Sadineni et al. (2023) | N/A | N/A | YES | N/A | N/A | N/A | YES
Alkhalil et al. (2019) | N/A | N/A | YES | N/A | N/A | YES | N/A
Wang et al. (2018) | YES | N/A | YES | YES | N/A | YES | N/A
Elkhodr and Alsinglawi (2020) | N/A | N/A | N/A | N/A | YES | N/A | N/A
Table 8. Attacks studied in the selected security techniques.

Reference | Data Attacks | Provenance Attacks
---|---|---
| Drop | Replay | Forgery | Modification | Eavesdrop | Record drop | Replay | Forging | Modification |  Chain tampering | Inference
Lim et al. (2010) | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |  ✗ | ✗
Wang et al. (2016a) |  1F5F8 |  1F5F8 |  1F5F8 |  1F5F8 | ✗ |  1F5F8 |  1F5F8 |  1F5F8 |  1F5F8 |  ✗ | ✗
Chia et al. (2017) |  1F5F8 |  1F5F8 |  1F5F8 |  1F5F8 | ✗ |  1F5F8 |  1F5F8 |  1F5F8 |  1F5F8 |  ✗ | ✗
Suhail et al. (2018) |  1F5F8 |  1F5F8 |  1F5F8 | ✗ |  1F5F8 |  1F5F8 |  1F5F8 |  1F5F8 | ✗ |  ✗ | ✗
Xu et al. (2019) | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |  ✗ | ✗
Suhail et al. (2020) |  1F5F8 |  1F5F8 | ✗ | ✗ | ✗ |  1F5F8 |  1F5F8 | ✗ |  1F5F8 |  1F5F8 | ✗
Liu and Wu (2020) |  1F5F8 |  1F5F8 |  1F5F8 |  1F5F8 | ✗ | ✗ | ✗ | ✗ | ✗ |  ✗ | ✗
Tang and Keoh (2020) | ✗ | ✗ |  1F5F8 |  1F5F8 |  1F5F8 | ✗ | ✗ | ✗ | ✗ |  ✗ | ✗
Porkodi and Kesavaraja (2021) | ✗ | ✗ |  1F5F8 | ✗ | ✗ | ✗ | ✗ |  1F5F8 | ✗ |  ✗ | ✗
Xu et al. (2022) |  1F5F8 | ✗ |  1F5F8 |  1F5F8 | ✗ |  1F5F8 | ✗ |  1F5F8 |  1F5F8 |  ✗ |  1F5F8
Xu and Wang (2022) |  1F5F8 | ✗ | ✗ |  1F5F8 | ✗ |  1F5F8 | ✗ | ✗ |  1F5F8 |  ✗ | ✗
Sultana et al. (2015) |  1F5F8 |  1F5F8 | ✗ | ✗ |  1F5F8 |  1F5F8 | ✗ | ✗ | ✗ |  ✗ |  1F5F8
Siddiqui et al. (2019) |  1F5F8 |  1F5F8 | ✗ |  1F5F8 | ✗ | ✗ | ✗ | ✗ | ✗ |  ✗ | ✗
Harshan et al. (2020) | ✗ | ✗ |  1F5F8 |  1F5F8 |  1F5F8 | ✗ | ✗ |  1F5F8 |  1F5F8 |  ✗ |  1F5F8
Ali et al. (2014) | ✗ |  1F5F8 |  1F5F8 |  1F5F8 |  1F5F8 | ✗ |  1F5F8 |  1F5F8 | ✗ |  ✗ | ✗
Alam and Fahmy (2014) | ✗ |  1F5F8 |  1F5F8 |  1F5F8 | ✗ | ✗ |  1F5F8 |  1F5F8 |  1F5F8 |  ✗ | ✗
Kamal and Tariq (2018) | ✗ | ✗ |  1F5F8 | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |  ✗ | ✗
Baracaldo et al. (2017) | ✗ | ✗ | ✗ |  1F5F8 | ✗ | ✗ | ✗ | ✗ |  1F5F8 |  ✗ | ✗
Zeng et al. (2018) |  1F5F8 | ✗ | ✗ | ✗ |  1F5F8 | ✗ | ✗ | ✗ | ✗ |  ✗ | ✗
Javaid et al. (2018) |  1F5F8 | ✗ |  1F5F8 |  1F5F8 | ✗ |  1F5F8 | ✗ |  1F5F8 |  1F5F8 |  ✗ | ✗
Sigwart et al. (2020) | ✗ | ✗ |  1F5F8 |  1F5F8 |  1F5F8 | ✗ | ✗ |  1F5F8 |  1F5F8 |  ✗ | ✗
Liu et al. (2020) | ✗ | ✗ |  1F5F8 |  1F5F8 | ✗ | ✗ | ✗ |  1F5F8 |  1F5F8 |  ✗ | ✗
Siddiqui et al. (2020) | ✗ | ✗ |  1F5F8 |  1F5F8 | ✗ | ✗ | ✗ |  1F5F8 |  1F5F8 |  ✗ | ✗
Yin and Fu (2022) | ✗ | ✗ |  1F5F8 |  1F5F8 | ✗ | ✗ | ✗ |  1F5F8 |  1F5F8 |  ✗ | ✗
Sun et al. (2022) | ✗ | ✗ |  1F5F8 |  1F5F8 | ✗ | ✗ | ✗ |  1F5F8 |  1F5F8 |  ✗ | ✗
Chenli et al. (2022) | ✗ | ✗ |  1F5F8 |  1F5F8 | ✗ | ✗ | ✗ |  1F5F8 |  1F5F8 |  ✗ | ✗
Sultana et al. (2011) |  1F5F8 | ✗ | ✗ |  1F5F8 | ✗ | ✗ | ✗ | ✗ | ✗ |  ✗ | ✗
Aman et al. (2019) |  1F5F8 |  1F5F8 | ✗ |  1F5F8 |  1F5F8 |  1F5F8 |  1F5F8 | ✗ |  1F5F8 |  ✗ |  1F5F8
Aman et al. (2021) |  1F5F8 |  1F5F8 |  1F5F8 |  1F5F8 | ✗ | ✗ | ✗ | ✗ | ✗ |  ✗ | ✗
Hamadeh and Tyagi (2021) | ✗ | ✗ |  1F5F8 |  1F5F8 | ✗ | ✗ | ✗ |  1F5F8 |  1F5F8 |  ✗ | ✗
Chang et al. (2022) | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |  ✗ | ✗
Jaigirdar et al. (2023) | ✗ | ✗ |  1F5F8 |  1F5F8 | ✗ | ✗ | ✗ |  1F5F8 |  1F5F8 |  ✗ | ✗
Al-Rakhami and Al-Mashari (2022) | ✗ | ✗ |  1F5F8 |  1F5F8 | ✗ | ✗ | ✗ |  1F5F8 |  1F5F8 |  ✗ | ✗
Lomotey et al. (2019) | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |  ✗ | ✗
Lomotey et al. (2018) | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |  ✗ | ✗
Gao et al. (2013) | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |  ✗ | ✗
Sadineni et al. (2023) |  1F5F8 | ✗ |  1F5F8 |  1F5F8 | ✗ |  1F5F8 | ✗ |  1F5F8 |  1F5F8 |  ✗ | ✗
Alkhalil et al. (2019) | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |  ✗ | ✗
Wang et al. (2018) | ✗ | ✗ |  1F5F8 |  1F5F8 |  1F5F8 | ✗ | ✗ |  1F5F8 |  1F5F8 |  1F5F8 |  1F5F8
Elkhodr and Alsinglawi (2020) | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |  ✗ | ✗