# Privacy-Preserving Data Mining on Blockchain-Based WSNs
## 2 Related Work
### Privacy-Preserving Data Mining in WSNs  
Conventionally, DM on WSN data is realized in a centralized processing point external to the WSN. While it is simple to implement, such an approach raises privacy concerns due to the required relocation of sensor node data to the external system through the WSN, a multi-hop wireless network operated by resource-constrained devices. There are many studies addressing privacy concerns in WSNs [2; 3; 4], several of them indicating that, in addition to data privacy, contextual privacy must also be considered.  
A considerable amount of literature has been published on privacy-preserving data aggregation since it is particularly suited to WSNs' characteristics [5; 6]. The concept is based on aggregating data as it travels through the multi-hop network towards the sink node, and on applying privacy-preservation techniques during aggregation points to prevent the disclosure of the data of individual sensor nodes. Although the data aggregate does not disclose individual sensor privacy, aggregating the data may also reduce the knowledge obtainable using DM.  
Local differential privacy (LDP) is well established in DM applications that require the privacy of individual data sources [7]. Data is encoded or perturbed at the origin or intermediate point, and then sent to the consumer. Therefore, LDP techniques also leverage the trade-off between privacy and data utility. The authors in [8] discuss the application of LDP in decentralized IoT networks. An LDP IoT framework is presented in [9]; however, LDP is applied only at edge servers.  
Homomorphic encryption was proposed for preserving privacy, both in DM [10] and WSN applications [6]. Privacy is assured since computations are performed on cryptographically secured data without decryption. However, the technique is computationally intensive [11; 12], and reasonable solutions for WSNs are designed only to compute addition and multiplication on encrypted data.  
Secure multi-party computation (SMC) [13] allows multiple parties to jointly compute a function over their inputs while keeping those inputs private. Many studies [10; 14] use the SMC technique to preserve the privacy of individual data sources during DM operations. However, traditional solutions relying on SMC require high computation and communication costs; thus, they are not convenient for application in WSNs. Therefore, SMC in WSNs is limited to low-complexity tasks, such as data aggregation [15].  
Data mining algorithms that are performed over a WSN are inherently distributed, which has shifted the focus from traditional data mining (DM) approaches to the paradigm of distributed data mining (DDM) in recent years. The authors of [16] provide a relatively recent survey of state-of-the-art DDM techniques, focusing also on privacy-preserving distributed data mining (PPDDM). On the other hand, [17] introduces the notion of decentralized spatial data mining (DSDM), where individual sensor-enabled computing nodes possess only local knowledge about their immediate neighborhood, but derive global knowledge through local collaboration and information exchange. This is especially relevant for our research, where we aim at monitoring indoor air quality over a WSN.