## 1 Introduction  
The increasing prevalence of the IoT in our daily lives opens a wealth of opportunities to create new services, many of them personalized and customized for individuals, provided that the privacy of individual and sensible data are maintained in the process of constructing digests that can be used for public benefits, such as planning, policy making [1], smart agriculture [2], and healthcare [3], among others. However, as the IoT becomes more integrated into our daily routines, the critical issue of data privacy grows. IoT devices and their associated data are prone to significant vulnerabilities from a modern information security perspective [4]. The issue arises when raw data are collected and transmitted without sufficient privacy safeguards. This situation can result in dangers such as unauthorized virtual profiling, which poses serious threats to essential privacy needs and the confidence of the public [5]. To counter these risks, ensuring the privacy of data collected from IoT devices while using them for social profit and the common good can be effectively achieved through secure multiparty computation (SMPC), secret-sharing protocols, and masking through one-time pads (OTPs). Secure multiparty computation is a robust method that allows multiple parties to jointly compute a function over their private inputs without revealing them to each other. This security is a significant advancement over traditional lightweight key management systems, which, while less resource intensive, may not offer comparable security levels; see, for example, Ref. [6]. SMPC ensures that each participant's data remain concealed throughout the computation process, significantly improving privacy and security. SMPC can also be used in distributed scenarios with other security-preserving techniques, such as secret sharing and oblivious transfer, enabling joint computation without compromising individual data privacy. Secret-sharing schemes allocate a secret to several participants, requiring a specific minimum number of shares to be gathered for reconstruction. These protocols are crucial for cryptographic applications, starting with the pioneering contributions of [PERSON] and [PERSON] in 1976 [7]. An example of a protocol that uses secure multiparty computation and secret sharing in a distributed environment that maintains user privacy while decreasing communication and computation expenses is discussed in [PERSON]'s work [8]. On the other hand, oblivious transfer (OT) allows a party to send one of many pieces of information to another without revealing which piece was sent; see, for example, ref. [9]. Furthermore, employing masking via one-time pads involves encrypting data through XOR operations with a key known exclusively to the communicating parties, ensuring a robust level of security given that the key is utilized only once. When integrated with SMPC, this approach creates an effective framework for protecting sensitive information, as is the case in policy formulation and healthcare applications, where protecting sensitive personal data during analysis and decision-making processes is mandatory. The combination of these techniques ensures that, even if part of the system is compromised, it would be difficult for third parties to deduce confidential information as it would require considerable computational resources or processing times for full disclosure of protected data, thus supporting the ethical and secure use of IoT data for public benefit. Numerous proposals for collaborative machine learning (ML) models exist where parties want to keep their data private, for example, in text classification for speech detection [10] and machine learning with sensitive medical data [11], in ensuring privacy in training deep networks on decentralized data [12], and in the use of blockchain technology to enhance the security of distributed systems [13].