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It's scary that it doesn't surprise me anymore. Especially social networks are considered most lucrative in terms of targeted marketing and data mining, and it

It's scary that it doesn't surprise me anymore. Especially social networks are considered most lucrative in terms of targeted marketing and data mining, and it's obvious why. Social networking remains a big deal, it's almost mandatory to have some social networking footprint online, or else you miss out on social life. Why is it still OK to trade data distilled from social media accounts? It's not! One of the many reasons and implications are in that article. Is independent social media possible? How to fund basic service infrastructure if not by running online ads, or trading user data? Is decentralized social media feasible, and who maintains a decentralized service if it is? EDIT: If an app developer wants to analyze how the app performs, why share most intimate user data with third parties, Facebook being one of them?

inetknght 9y ago
> How to fund basic service infrastructure if not by running online ads, or trading user data? Subscriptions, combined with community awareness advertisements showing where users' money is going to (and where it is not going to) and why it benefits them.
- dschuetz 9y ago
  Yes, that's usually how it works with Netflix, Spotify and similar single-purpose services. But social media? If you take social network as a single-purpose service then there is no need for several or dozens of different similar services, because it wouldn't make sense as a social network, if you cannot interconnect them. Suppose you pay a subscription for one social network, you'll need to pay also for the other one, to get connected to people on that other social network! It needs to be one big single social network and everyone pays for one subscription. Anything else doesn't make sense, but a single social platform as a network is unthinkable. So, basically, subscription doesn't make sense for social networking.
  - iamatworknow 9y ago
    I don't understand your point here. Just like people pay separate subscription fees for Netflix and Hulu, which essentially provide the same service with different content, why wouldn't people pay separate subscription fees for Twitter and Facebook, if it were an option to avoid ads?
    - dschuetz 9y ago
      Because, in the end it would cost a fortune? Imagine that, most people only pay for one content provider. A social network is not a content provider, just a different kind of social network. If you cannot afford a subscription of some social network, you miss out. And that's not acceptable in terms of social life of a human being. Rich folks would be able to roam all networks, poor people wouldn't. So, if you only can afford some "cheap" networks, the whole thing ceases to make sense, as soon as you are barred from accessing other networks. Exclusiveness in social media is an oxymoron. See where this is going?
wackspurt 9y ago
I think that adoption of privacy preserving data aggregation/analysis will become the norm. The most immediate applications are 1) telemetry data that is used for monitoring (for example, Google Chrome uses differential privacy for collecting this data), and, 2) services like Google Maps and Tinder-like dating apps. In these applications, essential user information can be represented as integer/boolean values (is user present in location X? True or False. how old is the user? ...) Based on my limited understanding* of differential privacy, it falls short on exactness (of aggregate values) and robustness (against malicious clients). I've lately been studying the literature on function secret sharing and I think it is a better alternative to DP. Take this paper: https://www.henrycg.com/files/academic/pres/nsdi17prio-slide.. https://www.henrycg.com/files/academic/pres/nsdi17prio-slide.... Prio: Private, Robust and Scalable Computation of Aggregate Statistics Data collection and aggregation is performed by multiple servers. Every user splits up her response into multiple shares and sends one share to each server. I've understood how private sums can be computed. Let me explain it with a straw-man scheme. Example (slide 26): x_1 (user 1 is on Bay Bridge):- true == 1 == 15 + (-12) + (-2) x_2 (user 2 is on Bay Bridge):- false == 1 == (-10) + 7 + 3 ... If all users send shares of their data to the servers in this manner AND as long as at least one server doesn't reveal the identities of the people who sent it responses, the servers can exchange the sum of the shares they've received. Adding the three responses will allow the servers to infer that there are _ number of users on Bay Bridge without revealing their identities. This system can be made robust by using Secret-shared non-interactive proofs (SNIPs). This allows servers to test if Valid(X) holds without leaking X. The authors also bring up the literature on computing interesting aggregates using private sums: average, variance, most popular (approx.), min and max (approx.), quality of regression model R^2, least-squares regression, stochastic gradient descent. Bottom line: I found the discussion on deployment scenarios very interesting. Data servers with jurisdictional/geographical diversity, app store-app developer collaborations for eliminating risk in telemetry data analysis, enterprises contracting with external auditors for analyzing customer data, etc. * - I understand the randomized response and, to some extent, the RAPPOR technique (used for collecting Chrome telemetry data) but the other literature in that community goes over my head. * * - This technique is a black box to me at the moment.
- majos 9y ago

inetknght 9y ago

> How to fund basic service infrastructure if not by running online ads, or trading user data? Subscriptions, combined with community awareness advertisements showing where users' money is going to (and where it is not going to) and why it benefits them.

dschuetz 9y ago
Yes, that's usually how it works with Netflix, Spotify and similar single-purpose services. But social media? If you take social network as a single-purpose service then there is no need for several or dozens of different similar services, because it wouldn't make sense as a social network, if you cannot interconnect them. Suppose you pay a subscription for one social network, you'll need to pay also for the other one, to get connected to people on that other social network! It needs to be one big single social network and everyone pays for one subscription. Anything else doesn't make sense, but a single social platform as a network is unthinkable. So, basically, subscription doesn't make sense for social networking.
- iamatworknow 9y ago
  I don't understand your point here. Just like people pay separate subscription fees for Netflix and Hulu, which essentially provide the same service with different content, why wouldn't people pay separate subscription fees for Twitter and Facebook, if it were an option to avoid ads?
  - dschuetz 9y ago
    Because, in the end it would cost a fortune? Imagine that, most people only pay for one content provider. A social network is not a content provider, just a different kind of social network. If you cannot afford a subscription of some social network, you miss out. And that's not acceptable in terms of social life of a human being. Rich folks would be able to roam all networks, poor people wouldn't. So, if you only can afford some "cheap" networks, the whole thing ceases to make sense, as soon as you are barred from accessing other networks. Exclusiveness in social media is an oxymoron. See where this is going?

wackspurt 9y ago

I think that adoption of privacy preserving data aggregation/analysis will become the norm. The most immediate applications are 1) telemetry data that is used for monitoring (for example, Google Chrome uses differential privacy for collecting this data), and, 2) services like Google Maps and Tinder-like dating apps. In these applications, essential user information can be represented as integer/boolean values (is user present in location X? True or False. how old is the user? ...) Based on my limited understanding* of differential privacy, it falls short on exactness (of aggregate values) and robustness (against malicious clients). I've lately been studying the literature on function secret sharing and I think it is a better alternative to DP. Take this paper: https://www.henrycg.com/files/academic/pres/nsdi17prio-slide.. https://www.henrycg.com/files/academic/pres/nsdi17prio-slide.... Prio: Private, Robust and Scalable Computation of Aggregate Statistics Data collection and aggregation is performed by multiple servers. Every user splits up her response into multiple shares and sends one share to each server. I've understood how private sums can be computed. Let me explain it with a straw-man scheme. Example (slide 26): x_1 (user 1 is on Bay Bridge):- true == 1 == 15 + (-12) + (-2) x_2 (user 2 is on Bay Bridge):- false == 1 == (-10) + 7 + 3 ... If all users send shares of their data to the servers in this manner AND as long as at least one server doesn't reveal the identities of the people who sent it responses, the servers can exchange the sum of the shares they've received. Adding the three responses will allow the servers to infer that there are _ number of users on Bay Bridge without revealing their identities. This system can be made robust by using Secret-shared non-interactive proofs (SNIPs). This allows servers to test if Valid(X) holds without leaking X. The authors also bring up the literature on computing interesting aggregates using private sums: average, variance, most popular (approx.), min and max (approx.), quality of regression model R^2, least-squares regression, stochastic gradient descent. Bottom line: I found the discussion on deployment scenarios very interesting. Data servers with jurisdictional/geographical diversity, app store-app developer collaborations for eliminating risk in telemetry data analysis, enterprises contracting with external auditors for analyzing customer data, etc. * - I understand the randomized response and, to some extent, the RAPPOR technique (used for collecting Chrome telemetry data) but the other literature in that community goes over my head. * * - This technique is a black box to me at the moment.

majos 9y ago