{"id":2117,"date":"2014-06-24T15:30:11","date_gmt":"2014-06-24T15:30:11","guid":{"rendered":"http:\/\/kasperskydaily.com\/b2b\/?p=2117"},"modified":"2020-02-27T03:45:05","modified_gmt":"2020-02-26T16:45:05","slug":"big-deal-when-machines-know-better","status":"publish","type":"post","link":"https:\/\/www.kaspersky.com.au\/blog\/big-deal-when-machines-know-better\/2117\/","title":{"rendered":"Big Deal: when Machines know better"},"content":{"rendered":"

This post is the part of Big Data series, being published this week in Kaspersky Business. The first introductory post is available <\/em>here<\/em><\/a>.<\/em><\/p>\n

Target retail chain made headlines late last year due to the immense leak of the shoppers\u2019 payment information: a sophisticated and stealthy PoS malware had been used to steal data on ~70 million people. But a couple of years prior Target was extensively covered in New York Times\u2019 article How Companies Learn Your Secrets<\/a>. Target\u2019s analysts and marketers learned how to identify mothers-to-be in order to barrage them with the ads and offers related to their status: prenatal vitamins, maternity clothing, etc. Pregnant women are a golden vein for merchants, since they are \u2013 for a good reason \u2013 guaranteed spenders<\/em>.\u00a0<\/p>

Target\u2019s algorithm identifying pregnant women was a bit too exact. \u2013 @bigdata<\/p>Tweet<\/a><\/blockquote>\n

Target has a nation-wide database on its buyers \u2013 one of tremendous size, as one may guess. The company analysts have created a model which allowed them to identify \u201cmost likely pregnant\u201d women via a number of circumstantial features and subtle changes in their buying patterns. For instance, some women load up on supplements like calcium, magnesium and zinc the first 20 weeks of their pregnancy, switch to scent-free soap, etc.<\/p>\n

\n<\/p>

\u00a0<\/p>\n

The model of detecting pregnancy (without any, well, effort<\/em> from the buyers themselves) appeared to be very exact. Too exact, even: one day an angry father came to a Target store and demanded that a manager explain to him, why his daughter in high school received an advertising package for future mothers? A few days after, however, this angry father was apologizing to the store managers: he had just found out that his daughter was indeed pregnant.<\/p>\n

Target\u2019s analytics algorithm found this out first, which was a big win for the company marketers (sort of). But what was it for that girl? Apparently she had a different view on her privacy than Target\u2019s \u201cMachine\u201d.<\/p>\n

As shown above, Big Data may help to extract<\/em> a dire lot of personal information about anyone, even such private matters as pregnancy, etc. It\u2019s not hard to imagine this information being used for something much less \u201cinnocent\u201d than inciting people into buying wares: industrial espionage, for instance? We hear a lot of stories about cybercriminals gathering all kind of personal information on their target business\u2019 employees in order to ensure their spear-phishing attacks\u2019 success<\/a> \u2013 so that they could next establish a foothold within the victim company\u2019s infrastructure and then exfiltrate whatever data they want. Why not use Big Data for the same purposes?<\/p>\n

Well, of course, Big Data isn\u2019t something accessible off the shelf for petty crooks, so this example may look a bit far-fetched. But then again, some 10-15 years ago, global cyber-espionage was a braver sci-fi author\u2019s fantasies. Then in 2012 we caught \u201cFlame\u201d<\/a>.\u00a0<\/p>

Global cyber-espionage used to be a brave fantasy. It\u2019s no longer the case.<\/p>Tweet<\/a><\/blockquote>\n

In other words, when it comes to a cut-throat competition between transnational corporations or covert cyberwarfare between mutually hostile nations, using Big Data for preparing a large-scale cyberattack with personal data as an Archimedes\u2019 lever doesn\u2019t look fantastic at all. Especially given what troves of personal data people \u2013 mid-to-top level corporate workers included \u2013 tend to feed to social networking sites these days.<\/p>\n

Aside from this, is there a way to ensure that Big Data mining won\u2019t yield any specific trade secrets for the wrong parties? Back in 2012, Steve Durbin, global vice president of the Information Security Forum (ISF), wrote<\/a> in Gigaom:<\/p>\n

Organizations are part of often complex, global and interdependent supply chains, which can be their weakest link. Information is what binds supply chains together, ranging from simple mundane data, to trade or commercial secrets and intellectual property \u2013 loss of which can lead to reputational damage and financial or legal penalties. There is a key role for information security in coordinating the contracting and provisioning of business relationships, including outsourcers, offshorers and supply chain and cloud providers.<\/em><\/p>\n

In other words, loose the grasp on information and \u2013 probably \u2013 lose it all. Now, the Bigger Data is, the stronger has to be the grasp. The question is how to do it in practice.<\/p>\n

Big Data Week<\/strong><\/p>\n

<< Previous Post<\/a> | Next Post >><\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

Big Data is widely used by businesses to gather information on their consumers. But it’s quite evident that the same Big Data may be used against the businesses as well.<\/p>\n","protected":false},"author":209,"featured_media":16102,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[1999,2994],"tags":[1042,2119,2120,1222],"class_list":{"0":"post-2117","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-business","8":"category-smb","9":"tag-big-data","10":"tag-data-marketing","11":"tag-information-security","12":"tag-target"},"hreflang":[{"hreflang":"en-au","url":"https:\/\/www.kaspersky.com.au\/blog\/big-deal-when-machines-know-better\/2117\/"},{"hreflang":"x-default","url":"https:\/\/www.kaspersky.com\/blog\/big-deal-when-machines-know-better\/2117\/"},{"hreflang":"en-za","url":"https:\/\/www.kaspersky.co.za\/blog\/big-deal-when-machines-know-better\/2117\/"}],"acf":[],"banners":"","maintag":{"url":"https:\/\/www.kaspersky.com.au\/blog\/tag\/big-data\/","name":"big data"},"_links":{"self":[{"href":"https:\/\/www.kaspersky.com.au\/blog\/wp-json\/wp\/v2\/posts\/2117","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.kaspersky.com.au\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.kaspersky.com.au\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.kaspersky.com.au\/blog\/wp-json\/wp\/v2\/users\/209"}],"replies":[{"embeddable":true,"href":"https:\/\/www.kaspersky.com.au\/blog\/wp-json\/wp\/v2\/comments?post=2117"}],"version-history":[{"count":3,"href":"https:\/\/www.kaspersky.com.au\/blog\/wp-json\/wp\/v2\/posts\/2117\/revisions"}],"predecessor-version":[{"id":26377,"href":"https:\/\/www.kaspersky.com.au\/blog\/wp-json\/wp\/v2\/posts\/2117\/revisions\/26377"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.kaspersky.com.au\/blog\/wp-json\/wp\/v2\/media\/16102"}],"wp:attachment":[{"href":"https:\/\/www.kaspersky.com.au\/blog\/wp-json\/wp\/v2\/media?parent=2117"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.kaspersky.com.au\/blog\/wp-json\/wp\/v2\/categories?post=2117"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.kaspersky.com.au\/blog\/wp-json\/wp\/v2\/tags?post=2117"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}