Scheme to Hide Sensitive Sequential Patterns Computer Science Essay
This paper presents the first permutation-based approach to avoid exposing sensitive sequential patterns, and develops an efficient and effective algorithm for generating permutations with minimal side effects and distortion. Sequence data is increasingly being shared to enable mining applications, in various domains, such as Exact algorithms hide the sensitive knowledge without any critical compromises, such as blocking non-sensitive patterns or the appearance of rare itemsets, among the frequent ones. First, we redefine the problem of hiding sequential patterns to capture the information loss caused by cleaning in terms of event distortion and lost, non-sensitive knowledge patterns. Second, we model sequences as graphs and propose two algorithms to solve the problem by operating on the graphs. We present a scheme for sequential decision making with a risk-sensitive objective and constraints in a dynamic environment. A neural network is trained as an approach to mapping from parameter space to space of risk and policy with risk-sensitive constraints. For a particular risk-sensitive problem, where the goal and objective are, a new structure is designed to facilitate the hiding process. An algorithm called HUS-Hiding is proposed to hide sequential patterns with high utility. HUS-Hiding was tested on six datasets in terms of runtime, memory usage and missing costs. HUS-Hiding is more effective than three state-of-the-art algorithms. Here are ten examples of computer science essay topics to get you started: The Impact of Artificial Intelligence on Society: Pros and Cons. Cybersecurity measures in cloud computing systems. The ethics of big data: privacy, bias and transparency. The future of quantum computing: opportunities and challenges. It is shown that most risk measures can be estimated using return variance and that, by virtue of the state augmentation transformation, practical problems modeled by Markov decision processes with stochastic rewards can be solved in a risk scenario. - sensitive scenario. We present a scheme for sequential decision making with a risk-sensitive, A-sequence database D. S n for sequential pattern mining consists of n input sequences where n ≥ 1, and an input sequence S i, 〈e i1, e i2, e im 〉 1 ≤ i ≤ n is an ordered list of m events where m ≥1. Each event \, e, ij \left 1\le i\le n,1\le j\le m\right \ is a non-empty set of items. Given two series, S a, 〈e a1, the main contributions of our work are listed as follows: 1. This paper investigates two GA-based sensitive information hiding models based on varied thresholds of sensitive patterns, which is more applicable is in real-world situations, especially in eHealth-based medical datasets. 2. As databases fit into ever-growing main memory, efficient memory-based discovery of sequential patterns will become possible. In this paper, we propose a memory indexing approach for fast sequential pattern mining, called MEMISP. During the whole process, MEMISP scans the sequence database only once to read data. 1. IntroductionMonitoring and detecting significant patterns in a network, such as: i the presence of persistent flows with a large data volume or ii a sudden increase in network traffic due to the emergence of new flows are essential for.