# Biblio

The authors have proposed the Fallback Control System (FCS) as a countermeasure after cyber-attacks happen in Industrial Control Systems (ICSs). For increased robustness against cyber-attacks, introducing multiple countermeasures is desirable. Then, an appropriate collaboration is essential. This paper introduces two FCSs in ICS: field network signal is driven FCS and analog signal driven FCS. This paper also implements a collaborative FCS by a collaboration function of the two FCSs. The collaboration function is that the analog signal driven FCS estimates the state of the other FCS. The collaborative FCS decides the countermeasure based on the result of the estimation after cyber-attacks happen. Finally, we show practical experiment results to analyze the effectiveness of the proposed method.

We present a new connection between self-adjusting binary search trees (BSTs) and heaps, two fundamental, extensively studied, and practically relevant families of data structures (Allen,Munro, 1978; Sleator, Tarjan, 1983; Fredman, Sedgewick, Sleator, Tarjan, 1986; Wilber, 1989; Fredman, 1999; Iacono, Özkan, 2014). Roughly speaking, we map an arbitrary heap algorithm within a broad and natural model, to a corresponding BST algorithm with the same cost on a dual sequence of operations (i.e. the same sequence with the roles of time and key-space switched). This is the first general transformation between the two families of data structures. There is a rich theory of dynamic optimality for BSTs (i.e. the theory of competitiveness between BST algorithms). The lack of an analogous theory for heaps has been noted in the literature (e.g. Pettie; 2005, 2008). Through our connection, we transfer all instance-specific lower bounds known for BSTs to a general model of heaps, initiating a theory of dynamic optimality for heaps. On the algorithmic side, we obtain a new, simple and efficient heap algorithm, which we call the smooth heap. We show the smooth heap to be the heap-counterpart of Greedy, the BST algorithm with the strongest proven and conjectured properties from the literature, conjectured to be instance-optimal (Lucas, 1988; Munro, 2000; Demaine et al., 2009). Assuming the optimality of Greedy, the smooth heap is also optimal within our model of heap algorithms. Intriguingly, the smooth heap, although derived from a non-practical BST algorithm, is simple and easy to implement (e.g. it stores no auxiliary data besides the keys and tree pointers). It can be seen as a variation on the popular pairing heap data structure, extending it with a ``power-of-two-choices'' type of heuristic. For the smooth heap we obtain instance-specific upper bounds, with applications in adaptive sorting, and we see it as a promising candidate for the long-standing question of a simpler alternative to Fibonacci heaps.

Hadoop is developed as a distributed data processing platform for analyzing big data. Enterprises can analyze big data containing users' sensitive information by using Hadoop and utilize them for their marketing. Therefore, researches on data encryption have been widely done to protect the leakage of sensitive data stored in Hadoop. However, the existing researches support only the AES international standard data encryption algorithm. Meanwhile, the Korean government selected ARIA algorithm as a standard data encryption scheme for domestic usages. In this paper, we propose a HDFS data encryption scheme which supports both ARIA and AES algorithms on Hadoop. First, the proposed scheme provides a HDFS block-splitting component that performs ARIA/AES encryption and decryption under the Hadoop distributed computing environment. Second, the proposed scheme provides a variable-length data processing component that can perform encryption and decryption by adding dummy data, in case when the last data block does not contains 128-bit data. Finally, we show from performance analysis that our proposed scheme is efficient for various applications, such as word counting, sorting, k-Means, and hierarchical clustering.

The challenge of maintaining confidentiality of stored and processed data in a remote database or cloud is quite urgent. Using homomorphic encryption may solve the problem, because it allows to compute some functions over encrypted data without preliminary deciphering of data. Fully homomorphic encryption schemes have a number of limitations such as accumulation of noise and increase of ciphertext extension during performing operations, the range of operations is limited. Nowadays a lot of homomorphic encryption schemes and their modifications have been investigated, so more than 25 reports on homomorphic encryption schemes have already been published on Cryptology ePrint Archive for 2016. We propose an overview of current Fully Homomorphic Encryption Schemes and analyze specific operations for databases which homomorphic cryptosystems allow to perform. We also investigate the possibility of sorting over encrypted data and present our approach to compare data encrypted by Multi-bit FHE scheme.