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At this point if the news that your phone company has been selling customer location data to bounty hunters surprises you, you may need to catch up on the last few years of privacy revelations. If you’re just generally suspicious of places that collect your data, congratulations on being right (again). AT&T, Sprint, T-Mobile, and Verizon are currently being sued for selling customer location data to third parties known as data brokers, who then sell the data to other people with an interest in finding you – especially the “kinda-sorta” officials like bail bondsmen and bounty hunters.

The short story

The Maryland-based ZLaw Firm filed a class action suit against the four big US mobile providers on May 2nd, 2023. They’re suing in the names of the company’s customers who were affected. Essentially, their lawsuit accuses these companies of providing access to real-time location data to companies that shouldn’t have had access. The suit covers a roughly four-year period from 2023 through 2023, though that doesn’t necessarily mean the activity was limited by these years.

Since it’s a class action lawsuit, affected individuals may be entitled to compensation, though more details on this will be forthcoming. The real goal here, however, is to get the big phone companies to stop selling sensitive customer information – or at least to be more careful with it.

What exactly has been going on?

Back in 2023 there was another scandal where it came out that Securus, a prison technology company, was giving low-level law enforcement officers access to the location of pretty much every phone on all of the major carriers. That level of surveillance usually requires a warrant in the US, but Securus was using an intermediary company called LocationSmart, which pretty much anyone could sign up for, even on a free trial account, to get access to the location of most cell phones being used in the U.S.

Generally, the data in question here isn’t your GPS data – it’s your approximate location as determined by the strength of different cell tower signals, which is something phone companies really need in order to provide service. However, some of the data available to bounty hunters was occasionally from GPS, meaning they could get your location down to a few meters.

A lot of other stuff happened around the 2023 location issue (including Securus being hacked, meaning access to their real-time tracking tools could have been in anyone’s hands for a while), but the reason it’s important to this story is that every carrier involved promised to fix these sorts of loopholes and stop giving sensitive data to sketchy third parties. That apparently hasn’t been going so well, since Motherboard was actually able to identify the general path the data took.

Here’s how the process seems to have been working:

A data aggregator (Zumigo, in this case) buys customer data from a telecom company. They then use this data for any number of things, including fraud prevention and possibly marketing.

Zumigo then sells off your data to other services, including, in this case, a company called Microbilt, which uses the access it buys from Zumigo to sell services, like background or credit check, or tracking people who might break their bail. Microbilt actually maintains price lists for services like these.

Whoever is using the service, like bounty hunters or landlords, pays for your cell phone data and gets to use it.

If all that seems a little Byzantine, it is, but though your data is bouncing through a lot of different companies, it’s all coming straight from the phone provider at the center. If they close off access to third parties who are misusing this data, there won’t be a problem anymore – but it seems like they aren’t.

Bounty hunters aren’t out to get me, why should I worry?

Okay, you’re not Han Solo, and your location data probably isn’t being pulled by anyone in particular, even though you did shoot first. There have been cases, though, of people with access to these tools using them for more off-the-clock activities, such as tracking girlfriends. That’s not something that’s likely to affect the general public, but the fact remains that we now have tools that allow certain people to find you pretty much anywhere, whether it’s a potential employer checking how often you visit a psychiatrist or a marketing company trying to build a better profile on you.

It’s not just tracking individual movements, either: location data that is gathered and analyzed in bulk can help identify trends in how people move. When anonymously gathered and properly used, this type of data can be very helpful in designing better systems, but when it’s firehosed out without much consideration as to whose hands it ends up in, it’s a breach of trust and just generally a bad idea.

Image credits: Sierpiński Pyramid from Above

Andrew Braun

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Openai Lawsuit: Chatgpt Makers Sued Over Alleged Data Usage

According to lawsuit filed in California, OpenAI used personal information including medical records, data on children and even accessed private conversations to train its AI models.

Not just ChatGPT, other tools such as Dall-E, Codex and Whisper were trained using data that was extracted in violation of privacy and security of real people.

ChatGPT responds to questions like a human being, writes essays like real people by emulating their experiences and even generates content as if it were penned by a historic figure. All of this comes from data that it has access to, and now its creator OpenAI has been accused of stealing personal information of real people, as per the lawsuit.

What does the lawsuit say?

The petitioners have remained anonymous since only their initials are mentioned in the 157-page lawsuit, but they have accused ChatGPT of posing a catastrophic risk. They have alleged that all that personally identifiable information was stolen from millions of people, to train the AI into being more human-like.

Basically OpenAI is accused of simply harvesting and using any piece of personal information that users provide on other platforms, without seeking consent or even approaching any individual. This means that ChatGPT and Dall-E are essentially generating profits based on the private lives of people who aren’t even aware of that.

The plaintiffs also mentioned that without the massive data pile, extracted unethically, OpenAI wouldn’t have been able to create generative AI that is bringing in billions in revenue. Physical location, chats, contact information, search history and even information from browsers had been taken without the knowledge of the users.

What do the plaintiffs demand?

According to the lawsuit, things get worse since OpenAI introduced its products to the market without even deploying the necessary safeguards to protect private data.

It calls for OpenAI to be transparent about its data collection methods, a compensation for the stolen information and an option for people to opt out of its data harvesting drive.

What is OpenAI’s track record on data privacy?

Before this reports have emerged that OpenAI also used data from YouTube, run by its rival Google, in order to train ChatGPT and other generative AI tools. The reports claimed that ChatGPT had secretly used YouTube since it is the single largest source of images, text transcripts and audio.

The allegations had come months after Google itself was accused of using data from ChatGPT to train its own AI bot called Bard.

ChatGPT had also been banned in Italy over data privacy concerns, as the government sought to prevent it from using the personal details of millions of citizens. But the ban was lifted months later, after Italian regulators were satisfied with the safeeguards that OpenAI had put in place.

But that wasn’t the end for OpenAI’s troubles, since Japan also issued a warning to the firm over data privacy concerns related to ChatGPT.

As for the lawsuit, OpenAI only states that it will collect email, payment information and name of its users whenever necessary. But the firm has never mentioned anything about the data sourced from other corners of the internet to train its model in the first place.

Smartphone Location Data Brokers Clash With Privacy Advocates Over Coronavirus

The collection and sale of smartphone location data has long been a source of controversy, especially given that most people don’t realise they are being tracked.


There have been a number of revelations about just how much location data is collected from smartphones, and how assurances about privacy may be far from true in practice.

When an app asks us for permission to access location data, the privacy policy generally states that this is anonymized. However, a New York Times investigation back in 2023 found that this isn’t necessarily the case – because specific, regular journeys easily identify individuals.

[One phone] leaves a house in upstate New York at 7 a.m. and travels to a middle school 14 miles away, staying until late afternoon each school day. Only one person makes that trip: Lisa Magrin, a 46-year-old math teacher. Her smartphone goes with her […]

The app tracked her as she went to a Weight Watchers meeting and to her dermatologist’s office for a minor procedure. It followed her hiking with her dog and staying at her ex-boyfriend’s home, information she found disturbing.

The report found that such data may be passed to as many as 40 different companies, and retained for years.

A follow-up report last year found that just one database contained location data for some 12 million Americans, and the NYT was able to track the movements of identifiable people in sensitive occupations.

We followed military officials with security clearances as they drove home at night. We tracked law enforcement officers as they took their kids to school […]

We spotted a senior official at the Department of Defense walking through the Women’s March [and] to a high school, homes of friends, a visit to Joint Base Andrews, workdays spent in the Pentagon and a ceremony at Joint Base Myer-Henderson Hall with President Barack Obama in 2023.

Smartphone location data for coronavirus tracking

Location data brokers say that the data they collect can help in the fight to limit the spread of the coronavirus, reports CNET.

Antonio Tomarchio, president of CuebIQ, says it isn’t providing data directly to any government agency, but he noted the COVID-19 Mobility Data Network has been in contact with policymakers dealing with the pandemic […]

“What we’re interested in, is trends in certain areas,” Tomarchio said. “If you have a big crowd in a park, this could be an indication that social distancing is not being respected.”

Critics, however, voice three objections. First, making the collection of such data seem acceptable because it’s being used to do good.

“This is an essentially corrupt ecosystem of companies spying on people without any meaningful understanding or meaningful consent,” said Jay Stanley, a senior policy analyst for the American Civil Liberties Union. “There is a danger that we allow these companies to validate these activities and to whitewash their reputation by repurposing their data for COVID-19.”

Second, the data may not be representative of the US population as a whole.

Certain apps and devices are more widely used by affluent and younger communities, Stacey Gray, senior counsel for the Future of Privacy Forum, told lawmakers. That could leave out some of the most vulnerable segments of the population.

“This includes underrepresentation of the elderly, very young or lowest-income people who do not own cellphones, or anyone who does not own a cellphone for other reasons, such as refusal on religious grounds,” Gray said in her testimony to Congress on Thursday.

Third, location data can be faked.

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Planning To Embrace Big Data? Here Are Real

Embracing analytics is much tougher when it comes to confronting what real-time challenges mean in this field. Your data has to be very appropriate at each moment. A single minute can cost you millions of dollars if misplaced. That’s how tough is analyzing big data, researching firms and putting it up all together into a single vertical. There are times when the minutes and seconds count in to be very crucial and no delays can be accepted. Like in flight landing, a single delay can harm lives. Big data analytics has been taking the picture up from science experiments, sensor detection, radar communications to social media activities and what not. Every single action is determined by what was done previously or how was it done earlier. With this forming the major layout, challenges are also gearing up. You need to have elite communications and predictions that could actually help. The intelligent transport systems, financial market trading or military operations demand real-time decisions to hit the performance factors. Now, these real-time insights could vary from organization to organization. Some might accept delays; some might look for slight delays to set the other variable on time and some might not even handle a nanosecond delay. This vague real-time definition is the biggest challenge to overcome. Big data differs from other forms of data as it is categorized with 3 V’s: Velocity, Variety and Volume. Data is usually collected from various sources and is then processed further according to the demands. Decision-making, organizing and accessing are some major works to be then related. Each application has got some architecture to follow. Such in a way that it is able to handle data spikes, shortage and is able to scale up with the growing data whenever necessary. We need an architecture that does not fade away with the usage like if it’s of no use after 1 year then probably having it right now isn’t a good option. So, scaling up with the architecture is again a challenge to tackle. Having the work done should not be the only goal when we are into analytics. Why because, if your system breaks at some time or is unable to process some data at any point of time your internal processes should have some backup. If the only goal you had was your external outlook, probably then maintenance would be an issue. If the system fails, there should be some good internal processes that could have the capability to back up the entire failure. If some random internal processes are there, then it would be very difficult to handle the issues at runtime. The last big challenge is to make this entire shift. Employees working on the old traditional work practices have to be somehow convinced to take up this way. There are a variety of escalations that can be stepped onto and huge tasks can be very efficiently affected by this. Gradually the entire paradigm would be stepping into this so why not now? If there are areas where the employees aren’t comfortable enough to look for, then trainings can be organized. Managers could probably scale up some traditional issues and make the entire team realize how analytics can help them ace. So, there has to be some change to be taken care of and it’s better to experience right now then to be late and struggle at further stages. Coming to the gist of what we have is: Real-time analytics demands much more of our efforts and hard work. There are still challenges we need to look after. The faster we grow at this, the better we would be later. So making a change is the need of the hour and scaling up with the market trends is what is required the most.

Being Paranoid About Data Accuracy!

As the day was coming to a close, I thought of fitting in another meeting. Two analysts in my team had been working for creating a data set for one of the predictive models we wanted to build. The combined work experience (on predictive modeling) between the analysts was ~ 5 years. I expected to breeze through the meeting and leave for the day.

So, the meeting started. Five minutes into the meeting and I knew that the meeting will take much longer than I initially thought!

The reason? Let’s go through the discussion as it happened:

Kunal: How many rows do you have in the data set?

Analyst 1: (After going through the data set) X rows

Kunal: How many rows do you expect?

Analyst 1 & 2: Blank look at their faces

Kunal: How many events / data points do you expect in the period / every month?

Analyst 1 & 2: …. (None of them had a clue)

The number of rows in the data set looked higher to me. The analysts had missed it clearly, because they did not benchmark it against business expectation (or did not have it in the first place). On digging deeper, we found that some events had multiple rows in the data sets and hence the higher number of rows.

A high percentage of analysts would have gone through similar experience at some point or other in their career.

At times, either due to timeline pressures or due to some other reason, we overlook doing basic sanity checks on the dataset we are working on. However, overlooking data accuracy at initial stages of project can prove very costly and hence usually it pays off to be paranoid about data accuracy.

I usually follow a simple framework for checking accuracy of data points. In this article, I’ll share the process, I typically use for checking data sanity. The framework goes top down, which suits well. If you have any glaring mistakes in the data sets, they would be evident early in the process.

Please note that the remaining article assumes that you are working on a structured data set. For unstructured datasets, while the principles would still apply, but the process would change.

[stextbox id=”section”]Step 1: Check number of columns and rows against expectations [/stextbox]

The first step as soon as you get any data set would be to check whether it has all the required rows and columns. Number of columns would be dictated by the number of hypothesis you have and the variables you would need to prove / dis-prove these hypothesis.

Number of rows on the other hand would be dictated by number of events you expect in the chosen period. The easiest benchmark would be based on your business understanding.

[stextbox id=”section”]Step 2: Check for duplicates at id level (and not for entire row) [/stextbox]

Once you are sure all the columns are present and number of rows look within expected range, quickly check for duplicates at level of your id (or the level at which the rows should be unique – it could be a combination of variables)

[stextbox id=”section”]Step 3: Check for blank columns, large % of blank data, high % of same data [/stextbox]

Now that you know all columns are there and there are no duplicates, look out if there are columns which are entirely blank. This can happen in case some join fails or in case there is some error in data extraction. If none of the columns are blank, look at the % of blank cases by each column and frequency distributions to find out if the same data is being repeated in more cases than expected.

[stextbox id=”section”]Step 4: Look at the distribution across various segments – check against business understanding and use pivot tables[/stextbox]

This step continues where 3 finishes. Instead of looking at frequencies of data points individually, look at their distributions. Do you expect normal, bi-polar or uniform distribution? Does the distribution look like what you expect?

[stextbox id=”section”]Step 5. Check outliers on all key variables – especially the computed ones[/stextbox]

Once the distributions look fine, check for outliers. Especially in cases where you have computed columns. Do the values on extreme look like as you had wanted? Make sure there are no divisions by zero, you have capped the values you would want to.

[stextbox id=”section”]Step 6: Check if values of a few test cases are in sync[/stextbox]

Once you have checked all the columns individually, check whether they are in sync with each other. Check whether various dates of cases are in chronological order (e.g. . Do the balances, spend and credit limit look in sync with each other for your credit card customers?

[stextbox id=”section”]Step 7: Pick up a few rows and check out their values in the underlying systems[/stextbox]

Once all the previous steps are done, it is time to check a few samples by querying the underlying systems or databases. If there was any error in data, you should have ideally identified it by now. This step just ensures that the data is as it was in the underlying systems.

Please note that some of these errors can be spotted through use of logs provided by your tool. Looking at the logs usually provides a lot of information about errors and warnings.

If you like what you just read & want to continue your analytics learning, subscribe to our emails or like our Facebook page.


How To Wake Someone Up Over The Phone

The most popular method of waking someone up through the phone is to call or text them. However, some people keep their phone silent or on don’t disturb mode while sleeping. When this happens, it can be quite difficult to wake them. 

As of right now, there is no built-in program that makes it possible to wake someone up through the phone. However, there are some workarounds that you could use to wake someone else through the phone. 

Here in this article, we have listed down some ways you can follow to wake someone up over the Phone. ​​

Even if the person you wish to wake up has turned off their notifications, there are ways you can still wake them up. You can use methods such as changing the Don’t Disturb settings or using the Find my phone feature to get the job done.

When you enabling this feature blocks all notifications, you could add an exception contact number. If the other person adds your number to the exception, your phone calls or chats will not be blocked, even if it’s in Don’t Disturb Mode.

Here we have listed down some steps you can follow to change the DND setting on different mobile devices (iPhone and Andriod).

On iPhone, you can directly select your contact number as an exception. 

Before you proceed with changing the setting, you might need to first add your phone number as the favorite contact on the other person’s phone. 

You might need to set your contact number as your favorite to select it in Don’t Disturb Settings.

After you have added your contact number as Favourite, you can proceed with making your number an exception. 

You can use the Find my phone app to play a sound and wake someone else when their Phone is on silent Mode or don’t disturb Mode. This feature is available on both Android and iPhone. 

Google Find My Device is an Android app that is pre-installed on some Android phones, while other Android users must download it from the Play Store. 

You can send sound alerts to wake someone up through Google Find My Device. To use this feature on Android, you must access the other person’s account details like email and Password for their Phone’s Google account. 

If you have the details, you can log in the information on your phone and use the Find my phone app to ring the Phone.

Note: Make sure the other party has an active internet connection, or the sound won’t play 

iPhone has an in-built Find My app. The app is automatically logged in with your iCloud information. Unlike Andriod, you cannot just add an email and password information to the Find my app. 

You can use two methods to play sound through the Find My app on your iPhone. 

Note: If the other party’s iPhone is not connected to the internet, the sound alert won’t play. 

If you have the login information of the other’s party’s iCloud, you need to sign out from your account and sign in with the other person’s iCloud login information on your iPhone. 

Note: Please note that if some of our files are not backed up in your iCloud, you may lose them while switching the iCloud account on your iPhone.

Family Sharing allows you to access your Family’s iCloud and device information on the Find my app. You don’t have to log out from your iCloud. You have to add the other party as a family. 

Here’re the steps you need to perform on your iPhone:

Here’re the steps you need to perform on other party’s iPhone:

After you send the invitation, the other person receiving it must follow the steps below to accept your invitation.

The other party’s name will appear in the Family. 

Now is the time to Play Sound in Find My iPhone. Here’re the steps you need to follow:

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