Searchable Symmetric Encryption (SSE) when deployed in the cloud allows one to query encrypted data without the risk of data leakage. Despite the widespread interest, existing surveys do not examine in detail how SSE’s underlying structures are designed and how these result in the many properties of a SSE scheme. This is the gap we seek to address, as well as presenting recent state-of-the-art advances on SSE. Specifically, we present a general framework and believe the discussions may lead to insights for potential new designs. We draw a few observations. First, most schemes use index table, where optimal index size and sublinear search can be achieved using an inverted index. Straightforward updating can only be achieved using direct index, but search time would be linear. A recent trend is the combinations of index table, and tree, deployed for efficient updating and storage. Secondly, mechanisms from related fields such as Oblivious RAM (ORAM) have been integrated to reduce leakages. However, using these mechanisms to minimise leakages in schemes with richer functionalities (e.g., ranked, range) is relatively unexplored. Thirdly, a new approach (e.g., multiple servers) is required to mitigate new and emerging attacks on leakage. Lastly, we observe that a proposed index may not be practically efficient when implemented, where I/O access must be taken into consideration.

No abstract

A smart home enables users to access devices such as lighting, HVAC, temperature sensors, and surveillance camera. It provides a more convenient and safe living environment for users. Security and privacy, however, is a key concern since information collected from these devices are normally communicated to the user through an open network (i. e. Internet) or system provided by the service provider. The service provider may store and have access to these information. Emerging smart home hubs such as Samsung SmartThings and Google Home are also capable of collecting and storing these information. Leakage and unauthorized access to the information can have serious consequences. For example, the mere timing of switching on/off of an HVAC unit may reveal the presence or absence of the home owner. Similarly, leakage or tampering of critical medical information collected from wearable body sensors can have serious consequences. Encrypting these information will address the issues, but it also reduces utility since queries is no longer straightforward. Therefore, we propose a privacy-preserving scheme, PrivHome. It supports authentication, secure data storage and query for smart home systems. PrivHome provides data confidentiality as well as entity and data authentication to prevent an outsider from learning or modifying the data communicated between the devices, service provider, gateway, and the user. It further provides privacy-preserving queries in such a way that the service provider, and the gateway does not learn content of the data. To the best of our knowledge, privacy-preserving queries for smart home systems has not been considered before. Under our scheme is a new, lightweight entity and key-exchange protocol, and an efficient searchable encryption protocol. Our scheme is practical as both protocols are based solely on symmetric cryptographic techniques. We demonstrate efficiency and effectiveness of our scheme based on experimental and simulation results, as well as comparisons to existing smart home security protocols.

No abstract

Please cite this article in press as: M.H. Ling, et al., Application of reinforcement learning for security enhancement in cognitive radio networks, Appl. Soft Comput. J. (2015), http://dx. a b s t r a c tCognitive radio network (CRN) enables unlicensed users (or secondary users, SUs) to sense for and opportunistically operate in underutilized licensed channels, which are owned by the licensed users (or primary users, PUs). Cognitive radio network (CRN) has been regarded as the next-generation wireless network centered on the application of artificial intelligence, which helps the SUs to learn about, as well as to adaptively and dynamically reconfigure its operating parameters, including the sensing and transmission channels, for network performance enhancement. This motivates the use of artificial intelligence to enhance security schemes for CRNs. Provisioning security in CRNs is challenging since existing techniques, such as entity authentication, are not feasible in the dynamic environment that CRN presents since they require pre-registration. In addition these techniques cannot prevent an authenticated node from acting maliciously. In this article, we advocate the use of reinforcement learning (RL) to achieve optimal or near-optimal solutions for security enhancement through the detection of various malicious nodes and their attacks in CRNs. RL, which is an artificial intelligence technique, has the ability to learn new attacks and to detect previously learned ones. RL has been perceived as a promising approach to enhance the overall security aspect of CRNs. RL, which has been applied to address the dynamic aspect of security schemes in other wireless networks, such as wireless sensor networks and wireless mesh networks can be leveraged to design security schemes in CRNs. We believe that these RL solutions will complement and enhance existing security solutions applied to CRN To the best of our knowledge, this is the first survey article that focuses on the use of RL-based techniques for security enhancement in CRNs.