## 1 Introduction  
With the explosive usage growth of intelligent connected devices and the Internet of Things (IoT) devices, such devices inevitably generate large amounts of data transmitted over networks. Since data traffic is increasing rapidly, content/network service providers must continuously install additional network facilities to keep up with such traffic growth [1]. Therefore, providers seek alternative solutions such as P2P (peer-to-peer network) and CDN (content delivery network) rather than just installing more facilities to handle data traffic increases. The typical approach of CDN and P2P is that users retrieve data from nearby and multiple sources that cache the data [2, 3]. Similar to CDN/P2P, a general approach for enhancing network performance is to retrieve data from nearby data holders that have previously obtained the data, not only from the original data source itself. The ICN (information-centric networking architecture), a future Internet architecture,follows such an approach to utilizing in-network caches [4; 5]. The NDN (named-data networking architecture) is one of the ICNs [6; 7]. The NDN makes network nodes such as network routers cache transmitted data. The network nodes caching the data directly respond to requests for the data as well as forward the requests to other nodes. Multi-access edge computing (MEC) architecture also follows such an approach [8]. The common factor of P2P, ICN/NDN, and MEC is that a data requestor can receive data from multiple nodes that have cached the requested data. In these cases, the problem is that the requestor cannot identify the cache nodes because the status of cached data in nodes is continuously changed. Especially, ICN/NDN does not provide the information of the cache nodes to end-users because of security concerns. Since the requestor receives the data from multiple unspecified and unknown nodes, it cannot verify whether attackers polluting the data are among the nodes. Hence, when using such technologies, the receiver must verify the received data. When using advanced network technologies such as ICN, one of the critical issues is how to authenticate received data efficiently. A data authentication scheme has been proposed in various ways because a data authentication scheme must consider service characteristics [9; 10]. A general method to authenticate data is to verify the digital signature of the data generated by the publisher of the data. A mechanism authenticating the signer's public key, such as PKI, is essential when verifying a digital signature. Additionally, a digital signature/verification scheme requires high computation overhead. It may not be suitable for ICN/NDN to utilize a digital signature/verification scheme in its original form to authenticate transmitted data. The ICN/NDN, as technologies of a transport/network layer, generally fragment the data into several small segments and send the segments. That is, they regard each data segment as a network traffic unit. If they utilize a digital signature/verification scheme, a data publisher must generate a digital signature for each segment of the data, and a data receiver must verify all signatures for using the data. However, such a process can cause long service delays. MHT (Merkle hash tree) is a valuable scheme to authenticate a data set [11]. Various services, such as blockchains [12; 13; 14; 15] and outsourcing services [16; 17], utilize MHT to verify a data set. The NDN also utilize MHT to authenticate segment sets. Some NDN applications such as connected vehicles, smart homes, and IoT-based disaster management need to share information as quickly as possible [18; 19; 20; 21]. However, the MHT-based data authentication scheme is still the major cause of NDN inefficiency [22]. Therefore, alternatives have been proposed [23; 24; 25]. There are two reasons for the inefficiency of MHT-based data authentication. First, the MHT must repeatedly compute the node values of the same internal nodes of its binary tree.