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Why Privacy Lawsuits against Apple Matter to Google

December 30, 2010
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Apple is being dragged into court over two separate class-action lawsuits filed last week. Both accuse Apple of violating the privacy of iPhone users. So why should Google care more about the suits than Apple?

One lawsuit, led by L.A. resident Jonathan Lalo, claims that Apple and at least eight app companies, including Room Candy Games, the company that makes Pimple Popper Lite — no, I’m not making this up — Pandora, Dictionary.com, the Weather Channel, Backflip Studios (Paper Toss), GOGII (TextPlus4), Outfit7 (Talking Tom Cat) and Sunstorm Interactive (Pumpkin Maker) are engaging in “privacy violations and unfair business practices.”

The second suit is led by three Texas men and one woman from California. In that suit, the plaintiffs claim that “personal, private information was obtained without their knowledge or consent … their personal property — their computer — was hijacked by Defendants and turned into a device capable of spying on their every online move.”

In a nutshell, both suits claim that apps can personally identify each user through a combination of each phone’s Unique Device ID (UDID) which cannot be changed, plus other data harvested from user activity.

“Class action” status means that more plaintiffs — theoretically any iPhone user — could get in on the suit, and more app developers could be added. Plaintiffs want both money and to punish Apple with fines.

The lawsuits come in the wake of a Wall Street Journal expose that detailed data collection by both Android and iPhone apps, data that was allegedly sent to advertisers. The lawsuits would probably not have happened without the Journal article raising the issue.

Apple is not being accused of gathering user data and passing it along to advertisers. In fact, such data collection without users’ consent is a violation of Apple’s privacy policy. Instead, Apple is accused of allowing the violation of its own policy.

And the data itself is not easily tracked back to individual users. While some data is collectable by some apps in an “anonymized” form, researchers have shown that it can often be “de-anonymized.” The theoretical risk is that app developers can divine political affiliation, sexual orientation, book purchases and the like, and associate that with a specific person and iPhone.

Such data collection is useful for monitoring user trends, and serving up relevant advertising, and that is the stated purpose. But it’s also potentially usable for all sorts of wicked schemes, from Big Brother monitoring of citizens to blackmail to the most likely, which is the sale of that information to third parties.

The fact is that acceptable policies and procedures — just how far cell phone companies, carriers, platform vendors and app developers can go in the collection and use of user data — has not yet been established. It will be established by lawsuits, such as the ones now confronting Apple.

Both Google and Apple seek to collect data to improve apps and advertising. While the Journal found that “among the apps tested, the iPhone apps transmitted more data than the apps on phones using Google Inc.’s Android operating system.” But it’s not clear if testing a different suite of apps would have produced a different result.

What is clear is that data collection is far more important to Google than it is to Apple.

How Apple and Google Make Money

Apple makes the vast majority of its revenue from the sale of actual physical handsets, and also from apps — developers who charge for apps must hand over about a third of that revenue to Apple.

A small but presumably growing percentage of iPhone revenue comes from advertising, specifically contextual advertising that requires user data.

But for Google, that’s the entire business model.

Google is an advertising company. It makes zero from handsets. The company instead plans to continue giving away the OS and its phone-accessible cloud services in order to lure in users who will be targeted by increasingly contextual advertising.

The “context” in “contextual advertising” comes from user data — location, preferences, history, social graph, schedule and more.

Sure, Google has other ways to monetize Android, including carrier deals, cloud-service up-selling and others. But by far the largest determinant of Google’s future success with Android is how thoroughly the world will allow Google’s advertising customers to exploit user data.

Google’s head of consumer products, Marissa Mayer, talked about Google’s plan for “contextual discovery” at LeWeb ‘10 conference in Paris this month. The idea is that Google will be able to mind and analyze user data, including location data, to provide “search results” even before the user decides to conduct a search. That same data could be used to serve up incredibly relevant ads, coupons and special offers.

I’m sure Apple would like to do something similar eventually. The point, however, is that mining user data is the most important aspect of Google’s plans to monetize Android. But it’s only a minor part of Apple’s plans.

If, in an extreme hypothetical example, all user data collection was banned, it would be the best thing that could possibly happen to Apple in its competition with Google. Apple would continue to make its billions on hardware and app royalties, while Google would be left with nothing except old-and-busted non-contextual ads.

Of course, this isn’t going to happen. But court cases will largely determine the legal limits of personal data collection, use and sharing.

The class-action lawsuits against Apple matter far more to Google than to Apple.

Besides, Apple’s remedies will be trivially easy to implement. In the worst case scenario, Apple may be faced with a tweak in its UDID-sharing policy, minor adjustments to code and possibly even the distribution of lunch money to the aggrieved parties.

But for Google, Android’s whole future rests on cases like these. Without contextual advertising, which requires the harvesting and processing of user data, Google would have to come up with a new Android business model.

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