{'fraction_non_alphanumeric': 0.01726263871763255, 'fraction_numerical': 0.0002466091245376079, 'mean_word_length': 4.802575107296137, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Recent progress in secure computation

Computer security has been an important issue since the first computer was developed. However, with the advent of faster and more accessible machines used by many users and large quantities of shared data, this issue has achieved far greater importance. It is no longer sufficient to rely on a system of password control through which a user is protected by having a 7-letter code known only to himself, since while this may, in the best case, prevent other users from directly accessing the users area, it does little to prevent indirect access. The potential dangers from such indirect access increase manyfold. In this survey, we shall discuss protection in three forms. The first is the privacy-certification problem for digital information. To maintain the privacy and authenticity of information cryptosystems and digitalized "signature" algorithms have been devised. The second involves the problems of unauthorized users gaining access to restricted data. In this case, it is necessary to discuss access control mechanisms that can be brought to bear in order to protect each users security. A third and far more subtle method of compromising a system is through what is called "statistical inference". Here, the user obtains information that is available to him legally and uses this information to infer information to which he has no access privileges. As more secure access-control mechanisms are proposed to guard from illegal access to protected data, it is this problem which looms as the major important problem of data security. And, this problem can never be totally solved since we must grant to authorized users access to data of this type. As an illustration of this problem, consider a problem faced by the census bureau ( or any other creator of administrative databases). In such a database, sensitive information is collected about a group of individuals while guaranteeing each individual that data collected about him will not be made available to users at large. However, in order to do research on large segments of the population, it is necessary for aggregated forms of this data to be made available to certain users. Suppose that a sociologist wishes to study correlations among a population with respect to various characteristics. Then, it might be necessary to give this sociologist access to the data. However, in order to guarantee each individual's privacy, we will wish to do this in a statistical manner. That is, we will refuse to answer questions about an individual or small set of individuals, but will make available information about larger segments of society in a manner that does not give information about any individual. And the problem arises as to how to insure that no malicious user can use this information in order to determine the characteristics of a single individual. A common method that has been proposed is to refuse access to information about any set of individuals which consists of too few people and in this manner restrict access to individual data. When data is given about a set of individuals, it will then be given in an aggregated form consisting of mean or median characteristics or counts of the number of people having a certain characteristic. However, as shown below, such a limitation is often not sufficient to guarantee individual privacy. Furthermore, refusing to answer a question often gives as much information as an aggregated answer since one might be able to infer information from the reason for a non-answer. Another area where this problem is of great significance is in the problem of medical record-keeping. Here, we may wish to track a set of people having a certain ailment in their early life (or people who have been exposed to certain phenomena) in order to determine long range effects of medications or exposures. In so doing, we want to make the data as helpful as possible to medical researchers while guaranteeing individual privacy. Because of the nature of such data, it is of great value to malicious users.