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7,101	The term "data mining" is a misnomer because the goal is the extraction of patterns and knowledge from large amounts of data, not the extraction of data itself. It also is a buzzword and is frequently applied to any form of large-scale data or information processing as well as any application of computer decision support system, including artificial intelligence and business intelligence. Often the more general terms data analysis and analytics—or, when referring to actual methods, artificial intelligence and machine learning—are more appropriate.
7,102	The actual data mining task is the semi-automatic or automatic analysis of large quantities of data to extract previously unknown, interesting patterns such as groups of data records , unusual records , and dependencies . This usually involves using database techniques such as spatial indices. These patterns can then be seen as a kind of summary of the input data, and may be used in further analysis or, for example, in machine learning and predictive analytics. For example, the data mining step might identify multiple groups in the data, which can then be used to obtain more accurate prediction results by a decision support system. Neither the data collection, data preparation, nor result interpretation and reporting is part of the data mining step, although they do belong to the overall KDD process as additional steps.
7,103	The difference between data analysis and data mining is that data analysis is used to test models and hypotheses on the dataset, e.g., analyzing the effectiveness of a marketing campaign, regardless of the amount of data. In contrast, data mining uses machine learning and statistical models to uncover clandestine or hidden patterns in a large volume of data.
7,104	The related terms data dredging, data fishing, and data snooping refer to the use of data mining methods to sample parts of a larger population data set that are too small for reliable statistical inferences to be made about the validity of any patterns discovered. These methods can, however, be used in creating new hypotheses to test against the larger data populations.
7,105	In the 1960s, statisticians and economists used terms like data fishing or data dredging to refer to what they considered the bad practice of analyzing data without an a-priori hypothesis. The term "data mining" was used in a similarly critical way by economist Michael Lovell in an article published in the Review of Economic Studies in 1983. Lovell indicates that the practice "masquerades under a variety of aliases, ranging from "experimentation" to "fishing" or "snooping" .
7,106	The term data mining appeared around 1990 in the database community, with generally positive connotations. For a short time in 1980s, the phrase "database mining"™, was used, but since it was trademarked by HNC, a San Diego-based company, to pitch their Database Mining Workstation; researchers consequently turned to data mining. Other terms used include data archaeology, information harvesting, information discovery, knowledge extraction, etc. Gregory Piatetsky-Shapiro coined the term "knowledge discovery in databases" for the first workshop on the same topic and this term became more popular in the AI and machine learning communities. However, the term data mining became more popular in the business and press communities. Currently, the terms data mining and knowledge discovery are used interchangeably.
7,107	The manual extraction of patterns from data has occurred for centuries. Early methods of identifying patterns in data include Bayes' theorem and regression analysis . The proliferation, ubiquity and increasing power of computer technology have dramatically increased data collection, storage, and manipulation ability. As data sets have grown in size and complexity, direct "hands-on" data analysis has increasingly been augmented with indirect, automated data processing, aided by other discoveries in computer science, specially in the field of machine learning, such as neural networks, cluster analysis, genetic algorithms , decision trees and decision rules , and support vector machines . Data mining is the process of applying these methods with the intention of uncovering hidden patterns. in large data sets. It bridges the gap from applied statistics and artificial intelligence to database management by exploiting the way data is stored and indexed in databases to execute the actual learning and discovery algorithms more efficiently, allowing such methods to be applied to ever-larger data sets.
7,108	The knowledge discovery in databases process is commonly defined with the stages:
7,109	It exists, however, in many variations on this theme, such as the Cross-industry standard process for data mining which defines six phases:
7,110	or a simplified process such as Pre-processing, Data Mining, and Results Validation.
7,111	Polls conducted in 2002, 2004, 2007 and 2014 show that the CRISP-DM methodology is the leading methodology used by data miners.
7,112	The only other data mining standard named in these polls was SEMMA. However, 3–4 times as many people reported using CRISP-DM. Several teams of researchers have published reviews of data mining process models, and Azevedo and Santos conducted a comparison of CRISP-DM and SEMMA in 2008.
7,113	Before data mining algorithms can be used, a target data set must be assembled. As data mining can only uncover patterns actually present in the data, the target data set must be large enough to contain these patterns while remaining concise enough to be mined within an acceptable time limit. A common source for data is a data mart or data warehouse. Pre-processing is essential to analyze the multivariate data sets before data mining. The target set is then cleaned. Data cleaning removes the observations containing noise and those with missing data.
7,114	Data mining involves six common classes of tasks:
7,115	Data mining can unintentionally be misused, producing results that appear to be significant but which do not actually predict future behavior and cannot be reproduced on a new sample of data, therefore bearing little use. This is sometimes caused by investigating too many hypotheses and not performing proper statistical hypothesis testing. A simple version of this problem in machine learning is known as overfitting, but the same problem can arise at different phases of the process and thus a train/test split—when applicable at all—may not be sufficient to prevent this from happening.
7,116	The final step of knowledge discovery from data is to verify that the patterns produced by the data mining algorithms occur in the wider data set. Not all patterns found by the algorithms are necessarily valid. It is common for data mining algorithms to find patterns in the training set which are not present in the general data set. This is called overfitting. To overcome this, the evaluation uses a test set of data on which the data mining algorithm was not trained. The learned patterns are applied to this test set, and the resulting output is compared to the desired output. For example, a data mining algorithm trying to distinguish "spam" from "legitimate" e-mails would be trained on a training set of sample e-mails. Once trained, the learned patterns would be applied to the test set of e-mails on which it had not been trained. The accuracy of the patterns can then be measured from how many e-mails they correctly classify. Several statistical methods may be used to evaluate the algorithm, such as ROC curves.
7,117	If the learned patterns do not meet the desired standards, it is necessary to re-evaluate and change the pre-processing and data mining steps. If the learned patterns do meet the desired standards, then the final step is to interpret the learned patterns and turn them into knowledge.
7,118	The premier professional body in the field is the Association for Computing Machinery's Special Interest Group on Knowledge Discovery and Data Mining . Since 1989, this ACM SIG has hosted an annual international conference and published its proceedings, and since 1999 it has published a biannual academic journal titled "SIGKDD Explorations".
7,119	Computer science conferences on data mining include:
7,120	Data mining topics are also present in many data management/database conferences such as the ICDE Conference, SIGMOD Conference and International Conference on Very Large Data Bases.
7,121	There have been some efforts to define standards for the data mining process, for example, the 1999 European Cross Industry Standard Process for Data Mining and the 2004 Java Data Mining standard . Development on successors to these processes was active in 2006 but has stalled since. JDM 2.0 was withdrawn without reaching a final draft.
7,122	For exchanging the extracted models—in particular for use in predictive analytics—the key standard is the Predictive Model Markup Language , which is an XML-based language developed by the Data Mining Group and supported as exchange format by many data mining applications. As the name suggests, it only covers prediction models, a particular data mining task of high importance to business applications. However, extensions to cover subspace clustering have been proposed independently of the DMG.
7,123	Data mining is used wherever there is digital data available. Notable examples of data mining can be found throughout business, medicine, science, finance, construction, and surveillance.
7,124	While the term "data mining" itself may have no ethical implications, it is often associated with the mining of information in relation to user behavior .
7,125	The ways in which data mining can be used can in some cases and contexts raise questions regarding privacy, legality, and ethics. In particular, data mining government or commercial data sets for national security or law enforcement purposes, such as in the Total Information Awareness Program or in ADVISE, has raised privacy concerns.
7,126	Data mining requires data preparation which uncovers information or patterns which compromise confidentiality and privacy obligations. A common way for this to occur is through data aggregation. Data aggregation involves combining data together in a way that facilitates analysis . This is not data mining per se, but a result of the preparation of data before—and for the purposes of—the analysis. The threat to an individual's privacy comes into play when the data, once compiled, cause the data miner, or anyone who has access to the newly compiled data set, to be able to identify specific individuals, especially when the data were originally anonymous.
7,127	It is recommended to be aware of the following before data are collected:
7,128	Data may also be modified so as to become anonymous, so that individuals may not readily be identified. However, even "anonymized" data sets can potentially contain enough information to allow identification of individuals, as occurred when journalists were able to find several individuals based on a set of search histories that were inadvertently released by AOL.
7,129	The inadvertent revelation of personally identifiable information leading to the provider violates Fair Information Practices. This indiscretion can cause financial, emotional, or bodily harm to the indicated individual. In one instance of privacy violation, the patrons of Walgreens filed a lawsuit against the company in 2011 for selling prescription information to data mining companies who in turn provided the data to pharmaceutical companies.
7,130	Europe has rather strong privacy laws, and efforts are underway to further strengthen the rights of the consumers. However, the U.S.–E.U. Safe Harbor Principles, developed between 1998 and 2000, currently effectively expose European users to privacy exploitation by U.S. companies. As a consequence of Edward Snowden's global surveillance disclosure, there has been increased discussion to revoke this agreement, as in particular the data will be fully exposed to the National Security Agency, and attempts to reach an agreement with the United States have failed.
7,131	In the United Kingdom in particular there have been cases of corporations using data mining as a way to target certain groups of customers forcing them to pay unfairly high prices. These groups tend to be people of lower socio-economic status who are not savvy to the ways they can be exploited in digital market places.
7,132	In the United States, privacy concerns have been addressed by the US Congress via the passage of regulatory controls such as the Health Insurance Portability and Accountability Act . The HIPAA requires individuals to give their "informed consent" regarding information they provide and its intended present and future uses. According to an article in Biotech Business Week, "'n practice, HIPAA may not offer any greater protection than the longstanding regulations in the research arena,' says the AAHC. More importantly, the rule's goal of protection through informed consent is approach a level of incomprehensibility to average individuals." This underscores the necessity for data anonymity in data aggregation and mining practices.
7,133	U.S. information privacy legislation such as HIPAA and the Family Educational Rights and Privacy Act applies only to the specific areas that each such law addresses. The use of data mining by the majority of businesses in the U.S. is not controlled by any legislation.
7,134	Under European copyright database laws, the mining of in-copyright works without the permission of the copyright owner is not legal. Where a database is pure data in Europe, it may be that there is no copyright—but database rights may exist, so data mining becomes subject to intellectual property owners' rights that are protected by the Database Directive. On the recommendation of the Hargreaves review, this led to the UK government to amend its copyright law in 2014 to allow content mining as a limitation and exception. The UK was the second country in the world to do so after Japan, which introduced an exception in 2009 for data mining. However, due to the restriction of the Information Society Directive , the UK exception only allows content mining for non-commercial purposes. UK copyright law also does not allow this provision to be overridden by contractual terms and conditions. Since 2020 also Switzerland has been regulating data mining by allowing it in the research field under certain conditions laid down by art. 24d of the Swiss Copyright Act. This new article entered into force on 1 April 2020.
7,135	The European Commission facilitated stakeholder discussion on text and data mining in 2013, under the title of Licences for Europe. The focus on the solution to this legal issue, such as licensing rather than limitations and exceptions, led to representatives of universities, researchers, libraries, civil society groups and open access publishers to leave the stakeholder dialogue in May 2013.
7,136	US copyright law, and in particular its provision for fair use, upholds the legality of content mining in America, and other fair use countries such as Israel, Taiwan and South Korea. As content mining is transformative, that is it does not supplant the original work, it is viewed as being lawful under fair use. For example, as part of the Google Book settlement the presiding judge on the case ruled that Google's digitization project of in-copyright books was lawful, in part because of the transformative uses that the digitization project displayed—one being text and data mining.
7,137	The following applications are available under free/open-source licenses. Public access to application source code is also available.
7,138	Carrot2: Text and search results clustering framework.
7,139	Chemicalize.org: A chemical structure miner and web search engine.
7,140	ELKI: A university research project with advanced cluster analysis and outlier detection methods written in the Java language.
7,141	GATE: a natural language processing and language engineering tool.
7,142	KNIME: The Konstanz Information Miner, a user-friendly and comprehensive data analytics framework.
7,143	Massive Online Analysis : a real-time big data stream mining with concept drift tool in the Java programming language.
7,144	MEPX: cross-platform tool for regression and classification problems based on a Genetic Programming variant.
7,145	mlpack: a collection of ready-to-use machine learning algorithms written in the C++ language.
7,146	NLTK : A suite of libraries and programs for symbolic and statistical natural language processing for the Python language.
7,147	OpenNN: Open neural networks library.
7,148	Orange: A component-based data mining and machine learning software suite written in the Python language.
7,149	PSPP: Data mining and statistics software under the GNU Project similar to SPSS
7,150	R: A programming language and software environment for statistical computing, data mining, and graphics. It is part of the GNU Project.
7,151	scikit-learn: An open-source machine learning library for the Python programming language;
7,152	Torch: An open-source deep learning library for the Lua programming language and scientific computing framework with wide support for machine learning algorithms.
7,153	UIMA: The UIMA is a component framework for analyzing unstructured content such as text, audio and video – originally developed by IBM.
7,154	Weka: A suite of machine learning software applications written in the Java programming language.
7,155	The following applications are available under proprietary licenses.
7,156	For more information about extracting information out of data , see:
7,157	The history of garbled circuits is complicated. The invention of garbled circuit was credited to Andrew Yao, as Yao introduced the idea in the oral presentation of a paper in FOCS'86. This was documented by Oded Goldreich in 2003. The first written document about this technique was by Goldreich, Micali, and Wigderson in STOC'87. The term "garbled circuit" was first used by Beaver, Micali, and Rogaway in STOC'90. Yao's protocol solving Yao's Millionaires' Problem was the beginning example of secure computation, yet it is not directly related to garbled circuits.
7,158	The oblivious transfer can be built using asymmetric cryptography like the RSA cryptosystem.
7,159	The protocol consists of 6 steps as follows:
7,160	The Boolean circuit for small functions can be generated by hand. It is conventional to make the circuit out of 2-input XOR and AND gates. It is important that the generated circuit has the minimum number of AND gates . There are methods that generate the optimized circuit in term of number of AND gates using logic synthesis technique. The circuit for the Millionaires' Problem is a digital comparator circuit . A full adder circuit can be implemented using only one AND gate and some XOR gates. This means the total number of AND gates for the circuit of the Millionaires' Problem is equal to the bit-width of the inputs.
7,161	Alice replaced 0 and 1 with the corresponding labels:
7,162	After this, Alice randomly permutes the table such that the output value cannot be determined from the row. The protocol's name, garbled, is derived from this random permutation.
7,163	This optimization reduces the size of garbled tables from 4 rows to 3 rows. Here, instead of generating a label for the output wire of a gate randomly, Alice generates it using a function of the input labels. She generates the output labels such that the first entry of the garbled table becomes all 0 and no longer needs to be sent:
7,164	Free XOR optimization implies an important point that the amount of data transfer and number of encryption and decryption of the garbled circuit protocol relies only on the number of AND gates in the Boolean circuit not the XOR gates. Thus, between two Boolean circuits representing the same function, the one with the smaller number of AND gates is preferred.
7,165	This optimization reduce the size of garbled table for AND gates from 3 row in Row Reduction to 2 rows. It is shown that this is the theoretical minimum for the number of rows in the garbled table, for a certain class of garbling techniques.
7,166	The Yao's Garbled Circuit is secure against a semi-honest adversary. This type of adversary follows the protocol and does not do any malicious behavior, but it tries to violate the privacy of the other party's input by scrutinizing the messages transmitted in the protocol.
7,167	It is more challenging to make this protocol secure against a malicious adversary that deviates from the protocol. One of the first solutions to make the protocol secure against malicious adversary is to use zero-knowledge proof to prevent malicious activities during the protocol. For years, this approach was considered more as theoretical solution than a practical solution because of complexity overheads of it. But, it is shown that it is possible to use it with just a small overhead. Another approach is using several GC for a circuit and verifying the correctness of a subset of them and then using the rest for the computation with the hope that if the garbler was malicious, it would be detected during the verification phase. Another solution is to make the garbling scheme authenticated such that the evaluator can verify the garbled circuit.
7,168	In light of the fact that one should be able to generate a proof of some statement only when in possession of certain secret information connected to the statement, the verifier, even after having become convinced of the statement's truth, should nonetheless remain unable to prove the statement to third parties.
7,169	In the plain model, nontrivial zero-knowledge proofs demand interaction between the prover and the verifier. This interaction usually entails the selection of one or more random challenges by the verifier; the random origin of these challenges, together with the prover's successful responses to them notwithstanding, jointly convince the verifier that the prover does possess the claimed knowledge. If interaction weren't present, then the verifier, having obtained the protocol's execution transcript—that is, the prover's one and only message—could replay that transcript to a third party, thereby convincing the third party that the verifier too possessed the secret information.
7,170	In the common random string and random oracle models, non-interactive zero-knowledge proofs exist, in light of the Fiat–Shamir heuristic. These proofs, in practice, rely on computational assumptions .
7,171	There is a well-known story presenting the fundamental ideas of zero-knowledge proofs, first published in 1990 by Jean-Jacques Quisquater and others in their paper "How to Explain Zero-Knowledge Protocols to Your Children". The two parties in the zero-knowledge proof story are Peggy as the prover of the statement, and Victor, the verifier of the statement.
7,172	In this story, Peggy has uncovered the secret word used to open a magic door in a cave. The cave is shaped like a ring, with the entrance on one side and the magic door blocking the opposite side. Victor wants to know whether Peggy knows the secret word; but Peggy, being a very private person, does not want to reveal her knowledge to Victor or to reveal the fact of her knowledge to the world in general.
7,173	They label the left and right paths from the entrance A and B. First, Victor waits outside the cave as Peggy goes in. Peggy takes either path A or B; Victor is not allowed to see which path she takes. Then, Victor enters the cave and shouts the name of the path he wants her to use to return, either A or B, chosen at random. Providing she really does know the magic word, this is easy: she opens the door, if necessary, and returns along the desired path.
7,174	However, suppose she did not know the word. Then, she would only be able to return by the named path if Victor were to give the name of the same path by which she had entered. Since Victor would choose A or B at random, she would have a 50% chance of guessing correctly. If they were to repeat this trick many times, say 20 times in a row, her chance of successfully anticipating all of Victor's requests would be reduced to 1 in 220, or 9.56 × 10−7.
7,175	Thus, if Peggy repeatedly appears at the exit Victor names, he can conclude that it is extremely probable that Peggy does, in fact, know the secret word.
7,176	One side note with respect to third-party observers: even if Victor is wearing a hidden camera that records the whole transaction, the only thing the camera will record is in one case Victor shouting "A!" and Peggy appearing at A or in the other case Victor shouting "B!" and Peggy appearing at B. A recording of this type would be trivial for any two people to fake . Such a recording will certainly never be convincing to anyone but the original participants. In fact, even a person who was present as an observer at the original experiment would be unconvinced, since Victor and Peggy might have orchestrated the whole "experiment" from start to finish.
7,177	Further, if Victor chooses his A's and B's by flipping a coin on-camera, this protocol loses its zero-knowledge property; the on-camera coin flip would probably be convincing to any person watching the recording later. Thus, although this does not reveal the secret word to Victor, it does make it possible for Victor to convince the world in general that Peggy has that knowledge—counter to Peggy's stated wishes. However, digital cryptography generally "flips coins" by relying on a pseudo-random number generator, which is akin to a coin with a fixed pattern of heads and tails known only to the coin's owner. If Victor's coin behaved this way, then again it would be possible for Victor and Peggy to have faked the experiment, so using a pseudo-random number generator would not reveal Peggy's knowledge to the world in the same way that using a flipped coin would.
7,178	Notice that Peggy could prove to Victor that she knows the magic word, without revealing it to him, in a single trial. If both Victor and Peggy go together to the mouth of the cave, Victor can watch Peggy go in through A and come out through B. This would prove with certainty that Peggy knows the magic word, without revealing the magic word to Victor. However, such a proof could be observed by a third party, or recorded by Victor and such a proof would be convincing to anybody. In other words, Peggy could not refute such proof by claiming she colluded with Victor, and she is therefore no longer in control of who is aware of her knowledge.
7,179	Imagine your friend "Victor" is red-green colour-blind and you have two balls: one red and one green, but otherwise identical. To Victor, the balls seem completely identical. Victor is skeptical that the balls are actually distinguishable. You want to prove to Victor that the balls are in fact differently coloured, but nothing else. In particular, you do not want to reveal which ball is the red one and which is the green.
7,180	Here is the proof system. You give the two balls to Victor and he puts them behind his back. Next, he takes one of the balls and brings it out from behind his back and displays it. He then places it behind his back again and then chooses to reveal just one of the two balls, picking one of the two at random with equal probability. He will ask you, "Did I switch the ball?" This whole procedure is then repeated as often as necessary.
7,181	By looking at the balls' colours, you can, of course, say with certainty whether or not he switched them. On the other hand, if the balls were the same colour and hence indistinguishable, there is no way you could guess correctly with probability higher than 50%.
7,182	Since the probability that you would have randomly succeeded at identifying each switch/non-switch is 50%, the probability of having randomly succeeded at all switch/non-switches approaches zero . If you and your friend repeat this "proof" multiple times , your friend should become convinced that the balls are indeed differently coloured.
7,183	The above proof is zero-knowledge because your friend never learns which ball is green and which is red; indeed, he gains no knowledge about how to distinguish the balls.
7,184	One well-known example of a zero-knowledge proof is the "Where's Waldo" example. In this example, the prover wants to prove to the verifier that they know where Waldo is on a page in a Where's Waldo? book, without revealing his location to the verifier.
7,185	The prover starts by taking a large black board with a small hole in it, the size of Waldo. The board is twice the size of the book in both directions, so the verifier cannot see where on the page the prover is placing it. The prover then places the board over the page so that Waldo is in the hole.
7,186	The verifier can now look through the hole and see Waldo, but they cannot see any other part of the page. Therefore, the prover has proven to the verifier that they know where Waldo is, without revealing any other information about his location.
7,187	This example is not a perfect zero-knowledge proof, because the prover does reveal some information about Waldo's location, such as his body position. However, it is a decent illustration of the basic concept of a zero-knowledge proof.
7,188	A zero-knowledge proof of some statement must satisfy three properties:
7,189	The first two of these are properties of more general interactive proof systems. The third is what makes the proof zero-knowledge.
7,190	We can apply these ideas to a more realistic cryptography application. Peggy wants to prove to Victor that she knows the discrete log of a given value in a given group.
7,191	Thus, a cheating prover has a 0.5 probability of successfully cheating in one round. By executing a large enough number of rounds, the probability of a cheating prover succeeding can be made arbitrarily low.
7,192	Peggy proves to know the value of x .
7,193	The following scheme is due to Manuel Blum.
7,194	In this scenario, Peggy knows a Hamiltonian cycle for a large graph G. Victor knows G but not the cycle Finding a Hamiltonian cycle given a large graph is believed to be computationally infeasible, since its corresponding decision version is known to be NP-complete. Peggy will prove that she knows the cycle without simply revealing it .
7,195	To show that Peggy knows this Hamiltonian cycle, she and Victor play several rounds of a game:
7,196	It is important that the commitment to the graph be such that Victor can verify, in the second case, that the cycle is really made of edges from H. This can be done by, for example, committing to every edge separately.
7,197	If Peggy does know a Hamiltonian cycle in G, she can easily satisfy Victor's demand for either the graph isomorphism producing H from G or a Hamiltonian cycle in H .
7,198	Peggy's answers do not reveal the original Hamiltonian cycle in G. Each round, Victor will learn only H's isomorphism to G or a Hamiltonian cycle in H. He would need both answers for a single H to discover the cycle in G, so the information remains unknown as long as Peggy can generate a distinct H every round. If Peggy does not know of a Hamiltonian cycle in G, but somehow knew in advance what Victor would ask to see each round then she could cheat. For example, if Peggy knew ahead of time that Victor would ask to see the Hamiltonian cycle in H then she could generate a Hamiltonian cycle for an unrelated graph. Similarly, if Peggy knew in advance that Victor would ask to see the isomorphism then she could simply generate an isomorphic graph H . Victor could simulate the protocol by himself because he knows what he will ask to see. Therefore, Victor gains no information about the Hamiltonian cycle in G from the information revealed in each round.
7,199	If Peggy does not know the information, she can guess which question Victor will ask and generate either a graph isomorphic to G or a Hamiltonian cycle for an unrelated graph, but since she does not know a Hamiltonian cycle for G she cannot do both. With this guesswork, her chance of fooling Victor is 2−n, where n is the number of rounds. For all realistic purposes, it is infeasibly difficult to defeat a zero-knowledge proof with a reasonable number of rounds in this way.
7,200	Different variants of zero-knowledge can be defined by formalizing the intuitive concept of what is meant by the output of the simulator "looking like" the execution of the real proof protocol in the following ways:

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