{"id":35504,"date":"2019-10-31T22:04:42","date_gmt":"2019-10-31T19:04:42","guid":{"rendered":"https:\/\/prohoster.info\/blog\/google-otkryl-sistemu-dlya-analiza-naborov-dannyh-bez-narusheniya-konfedentsialnosti\/"},"modified":"2019-10-31T22:04:42","modified_gmt":"2019-10-31T19:04:42","slug":"google-otkryl-sistemu-dlya-analiza-naborov-dannyh-bez-narusheniya-konfedentsialnosti","status":"publish","type":"post","link":"https:\/\/prohoster.info\/en\/blog\/news\/google-otkryl-sistemu-dlya-analiza-naborov-dannyh-bez-narusheniya-konfedentsialnosti","title":{"rendered":"Google has launched a system for analyzing datasets without compromising privacy.","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

Google Inc. introduced<\/a><\/noindex> cryptographic protocol for confidential multiparty computation Private Join and Compute<\/a><\/noindex>, enabling analysis and computations on encrypted datasets from multiple participants while preserving each participant's data privacy (each participant cannot obtain information about other participants' data but can perform aggregate computations on them without decryption). The implementation code of the protocol is open<\/a><\/noindex> under the Apache 2.0 license.<\/p>\n

Private Join and Compute allows for the transfer of a private dataset to a third party that can analyze it and assess differences with its own dataset in an aggregated manner while not being able to learn the specific values of individual records. For example, it is possible to obtain information such as the number of matching identifiers with its own dataset and the sums of values of records with matching identifiers in the encrypted dataset. However, it is impossible to know which specific values and identifiers are present in the dataset.<\/p>\n

The Private Join and Compute protocol, also known as Private Intersection-Sum, established<\/a><\/noindex> is based on a combination of the protocol random oblivious transfer<\/a><\/noindex> (Random Oblivious Transfer), encrypted Bloom filters<\/a><\/noindex> and double masking Polyga \u2014 Hellman<\/a><\/noindex>.<\/p>\n

The proposed system can be useful, for instance, when one medical institution has information about patients' health conditions, while another has details about a newly prescribed preventive medication. The 'Private Join and Compute' protocol allows for the combining of encrypted datasets without revealing information, enabling the generation of overall statistics to determine whether the prescribed medication reduces morbidity. Another example, utilizing data from accident databases from the traffic police and databases on the use of advanced safety features in vehicles, can assess whether the introduction of these features impacts the number of accidents.<\/p>\n

Another example is when, based on the employee database of one company and purchase data from another, it is possible to determine how many employees from the first company made purchases at the second and for what amount. In the context of advertising networks, similar calculations can be made to assess the effectiveness of advertising campaigns, operating with lists of users who were shown the ad (or who clicked on the link) and who made purchases in the online store.<\/p>\n

<\/a><\/noindex><\/p>\n

Source: opennet.ru<\/a><\/p>","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"excerpt":{"rendered":"

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