Big data analytics has been regarded as a promising technology to yield better insights into future development by government and industry. Data collection and aggregation are necessary steps to enable data analysis. However, data may be dispersed across multiple places and in different formats. Even worse, data can be encrypted under different encryption mechanisms when different data owners try to secure their data. This makes data aggregation very difficult, if not impossible, especially when the encryption keys cannot be shared for various reasons. In this paper, we take the first step in addressing this problem. More specifically, we propose a new notion of cross-domain encryption switching that bridges two well-studied encryption mechanisms, namely traditional public key encryption and identity-based encryption. As of independent interest, our notion supports keyword search over encrypted data. We provide a provably-secure instantiation satisfying the notion, and further present the efficiency analysis to show the scalability. Our proposed scheme is applicable in multi-domain cloud storage system.

Outsourcing encrypted data to cloud servers that has been become a prevalent trend among Internet users to date. There is a long-list advantages of data outsourcing, such as the reduction cost of local data management. How to securely operate encrypted data (remotely), however, is the top-rank concern of data owner. Recently, Liang et al. proposed a novel encrypted cloud-based data share and search system without loss of privacy. The system allows users to flexibly search and share encrypted data as well as updating keyword field. However, the search complexity of the system is very inefficient, O(n d), where d is the total number of the system files and n is the size of query formula. This paper, for the first time, leverages the “oblivious cross search” technology in public key searchable encryption context to reduce the search complexity to only O(nf(w)), where f(w) is the number of files embedded with the “least frequent keyword” w. The new scheme maintains efficient encrypted data share and keyword field update as well. This paper further revisits the security models for payload security, keyword privacy and search token privacy (i.e. search pattern privacy) and meanwhile, presents security and efficiency analysis for the new scheme.

The ability of smart objects to stay connected to the Internet for purposes of transmitting and receiving data is referred to as the Internet of Things (IoT). As per recent estimates, the number of IoT devices will surpass 50 billion by 2020. Unsurprisingly, this mushrooming of IoT devices has drawn the attention of attackers who seek to exploit them for their own benefit, with the Mirai botnet being perhaps the most prominent example of IoT specific malware [1, 2]. Basically, IoT brings along a plethora of potential security and privacy risks to the end-users, including the unsanctioned access and abuse of private information, the enabling and strengthening of assaults against other systems, and the breeding of risks pertaining to personal safeness [3]. Especially, IoT facilitates the creation of an assortment of privacy risks to the consumer associated with the collection of personal and sensitive information, like their preferences, locations, habits, and so on. In the mid- or long-run these pieces of data can be used to, say, profile or impersonate the user or group of interest. On the other hand, such risks to security, privacy, and trust may significantly diminish end-user’s confidence in IoT and therefore impede its full realization.