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<\/a><\/span><\/div><\/div><\/div><\/div><\/div>PROTECTING PRIVACY ONLINE<\/h1><\/div><\/div><\/div><\/div><\/div>![](\"https:\/\/cecas.clemson.edu\/ideas\/wp-content\/uploads\/2021\/04\/privacy-mobile.jpg\" "\\"privacy-mobile\\"")
<\/span><\/div>PROTECTING PRIVACY ONLINE<\/h1><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div>\n![](\"https:\/\/cecas.clemson.edu\/ideas\/wp-content\/uploads\/2021\/03\/paul-alongi.jpg\" "\\"paul-alongi\\"")
<\/span><\/div>By Paul Alongi,<\/span><\/strong> Clemson University<\/span><\/p>\n<\/div>Many social media users know the feeling.<\/strong><\/p>\nYou\u2019re scrolling through photos of family and friends when you suddenly see an ad for a product that you\u2019ve been thinking about buying. You haven\u2019t told a single soul you\u2019ve wanted this product, and you haven\u2019t even searched for it on Google or Amazon.<\/p>\n
Yet there it is, glowing up at you from your smartphone screen, and you can\u2019t help but wonder, \u201cHow did they know?\u201d<\/p><\/h3><\/div>
T<\/span>he privacy concerns that come with the lightning-fast immediacy and convenience of internet-connected life have become a central focus for Bart Knijnenburg,<\/b> an assistant professor of human-centered computing at Clemson University.<\/p>\nIt\u2019s not practical for most people to simply go off the grid, totally disconnecting from all internet and social media activity, Knijnenburg said. But it is possible, he said, to find a trade-off between privacy and enjoying the benefits of online services.<\/p>\n
Knijnenburg is helping find a middle way with research that merges privacy, artificial intelligence and recommender systems.<\/p>\n<\/div><\/div><\/div><\/div><\/div>
![](\"https:\/\/cecas.clemson.edu\/ideas\/wp-content\/uploads\/2021\/04\/bart.jpg\" "\\"bart\\"")
<\/span><\/div>Bart Knijnenburg (Center), <\/b>Assistant Professor<\/p>\n<\/div>He has developed what he calls \u201cuser-tailored privacy\u201d that works on the same idea that Netflix uses to figure out your viewing preferences and then recommend shows you might like. A tailored privacy system tries to find out what your privacy preferences are and then give you the settings that fit those preferences.<\/p>\n
It helps offer alternatives to some common hurdles that users face when signing up for internet services. Many services ask users to agree to privacy policies that run several pages long, and many offer an overwhelming number of privacy settings.<\/p>\n<\/div><\/div><\/div><\/div><\/div>
<\/i>With every online purchase and filled out survey and Facebook post, we are slowly but steadily chipping away at our privacy. You know, once this kind of information is out there, you can never get it back.<\/h2><\/div><\/b><\/p>\n
Bart Knijnenburg, Assistant Professor<\/h3><\/div><\/div><\/div><\/div><\/div>
\u201cCompanies are giving people outrageous options of sharing their information so they get biased toward sharing more,\u201d Knijnenburg said. \u201cIt shows that people are not very capable of dealing with that deluge of privacy settings.\u201d<\/p>\n
In one study Knijnenburg led, he and his team showed participants more than 1,000 different ways a smart home could use and share data. Participants were asked if they would allow each scenario or not.<\/p>\n
The team did data analysis on those decisions and then clustered people into different groups. The team ended up with a set of privacy profiles it could offer users.<\/p>\n
\u201cWhat we essentially did is we used machine learning to translate people\u2019s privacy preferences into settings,\u201d he said. \u201cSo rather than you having to figure out what your preferences are and then set every single setting manually, now what you have to do is figure out which kind of person are you and click one button and make one choice about those preferences. Then every setting in the system gets tweaked automatically based on that analysis we did.\u201d<\/p>\n
<\/span><\/div>PROTECTING PRIVACY ONLINE<\/h1><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div>\n<\/span><\/div>By Paul Alongi,<\/span><\/strong> Clemson University<\/span><\/p>\n<\/div>Many social media users know the feeling.<\/strong><\/p>\nYou\u2019re scrolling through photos of family and friends when you suddenly see an ad for a product that you\u2019ve been thinking about buying. You haven\u2019t told a single soul you\u2019ve wanted this product, and you haven\u2019t even searched for it on Google or Amazon.<\/p>\n
Yet there it is, glowing up at you from your smartphone screen, and you can\u2019t help but wonder, \u201cHow did they know?\u201d<\/p><\/h3><\/div>
T<\/span>he privacy concerns that come with the lightning-fast immediacy and convenience of internet-connected life have become a central focus for Bart Knijnenburg,<\/b> an assistant professor of human-centered computing at Clemson University.<\/p>\nIt\u2019s not practical for most people to simply go off the grid, totally disconnecting from all internet and social media activity, Knijnenburg said. But it is possible, he said, to find a trade-off between privacy and enjoying the benefits of online services.<\/p>\n
Knijnenburg is helping find a middle way with research that merges privacy, artificial intelligence and recommender systems.<\/p>\n<\/div><\/div><\/div><\/div><\/div>
<\/span><\/div>Bart Knijnenburg (Center), <\/b>Assistant Professor<\/p>\n<\/div>He has developed what he calls \u201cuser-tailored privacy\u201d that works on the same idea that Netflix uses to figure out your viewing preferences and then recommend shows you might like. A tailored privacy system tries to find out what your privacy preferences are and then give you the settings that fit those preferences.<\/p>\n
It helps offer alternatives to some common hurdles that users face when signing up for internet services. Many services ask users to agree to privacy policies that run several pages long, and many offer an overwhelming number of privacy settings.<\/p>\n<\/div><\/div><\/div><\/div><\/div>
<\/i>With every online purchase and filled out survey and Facebook post, we are slowly but steadily chipping away at our privacy. You know, once this kind of information is out there, you can never get it back.<\/h2><\/div><\/b><\/p>\n
Bart Knijnenburg, Assistant Professor<\/h3><\/div><\/div><\/div><\/div><\/div>
\u201cCompanies are giving people outrageous options of sharing their information so they get biased toward sharing more,\u201d Knijnenburg said. \u201cIt shows that people are not very capable of dealing with that deluge of privacy settings.\u201d<\/p>\n
In one study Knijnenburg led, he and his team showed participants more than 1,000 different ways a smart home could use and share data. Participants were asked if they would allow each scenario or not.<\/p>\n
The team did data analysis on those decisions and then clustered people into different groups. The team ended up with a set of privacy profiles it could offer users.<\/p>\n
\u201cWhat we essentially did is we used machine learning to translate people\u2019s privacy preferences into settings,\u201d he said. \u201cSo rather than you having to figure out what your preferences are and then set every single setting manually, now what you have to do is figure out which kind of person are you and click one button and make one choice about those preferences. Then every setting in the system gets tweaked automatically based on that analysis we did.\u201d<\/p>\n
Many social media users know the feeling.<\/strong><\/p>\n You\u2019re scrolling through photos of family and friends when you suddenly see an ad for a product that you\u2019ve been thinking about buying. You haven\u2019t told a single soul you\u2019ve wanted this product, and you haven\u2019t even searched for it on Google or Amazon.<\/p>\n Yet there it is, glowing up at you from your smartphone screen, and you can\u2019t help but wonder, \u201cHow did they know?\u201d<\/p><\/h3><\/div> T<\/span>he privacy concerns that come with the lightning-fast immediacy and convenience of internet-connected life have become a central focus for Bart Knijnenburg,<\/b> an assistant professor of human-centered computing at Clemson University.<\/p>\n It\u2019s not practical for most people to simply go off the grid, totally disconnecting from all internet and social media activity, Knijnenburg said. But it is possible, he said, to find a trade-off between privacy and enjoying the benefits of online services.<\/p>\n Knijnenburg is helping find a middle way with research that merges privacy, artificial intelligence and recommender systems.<\/p>\n<\/div><\/div><\/div><\/div><\/div> Bart Knijnenburg (Center), <\/b>Assistant Professor<\/p>\n<\/div> He has developed what he calls \u201cuser-tailored privacy\u201d that works on the same idea that Netflix uses to figure out your viewing preferences and then recommend shows you might like. A tailored privacy system tries to find out what your privacy preferences are and then give you the settings that fit those preferences.<\/p>\n It helps offer alternatives to some common hurdles that users face when signing up for internet services. Many services ask users to agree to privacy policies that run several pages long, and many offer an overwhelming number of privacy settings.<\/p>\n<\/div><\/div><\/div><\/div><\/div> Bart Knijnenburg, Assistant Professor<\/h3><\/div><\/div><\/div><\/div><\/div> \u201cCompanies are giving people outrageous options of sharing their information so they get biased toward sharing more,\u201d Knijnenburg said. \u201cIt shows that people are not very capable of dealing with that deluge of privacy settings.\u201d<\/p>\n In one study Knijnenburg led, he and his team showed participants more than 1,000 different ways a smart home could use and share data. Participants were asked if they would allow each scenario or not.<\/p>\n The team did data analysis on those decisions and then clustered people into different groups. The team ended up with a set of privacy profiles it could offer users.<\/p>\n \u201cWhat we essentially did is we used machine learning to translate people\u2019s privacy preferences into settings,\u201d he said. \u201cSo rather than you having to figure out what your preferences are and then set every single setting manually, now what you have to do is figure out which kind of person are you and click one button and make one choice about those preferences. Then every setting in the system gets tweaked automatically based on that analysis we did.\u201d<\/p>\n<\/span><\/div>By Paul Alongi,<\/span><\/strong> Clemson University<\/span><\/p>\n<\/div><\/span><\/div>With every online purchase and filled out survey and Facebook post, we are slowly but steadily chipping away at our privacy. You know, once this kind of information is out there, you can never get it back.<\/h2><\/div>
<\/b><\/p>\n