Surveillance and privacy
Discussion of issues related to liberty and privacy, and especially how they are affected by and interrelated with data management and analytic technologies. Related subjects include:
One of the most important issues in privacy and surveillance is also one of the least-discussed — the use of new surveillance technologies in ordinary law enforcement. Reasons for this neglect surely include:
- Governments, including in the US, lie about this subject a lot. Indeed, most of the reporting we do have is exposure of the lies.
- There’s no obvious technology industry ox being gored. What I wrote in another post about Apple, Microsoft et al. upholding their customers’ rights doesn’t have a close analogue here.
One major thread in the United States is: Read more
Numerous tussles fit the template:
- A government wants access to data contained in one or more devices (mobile/personal or server as the case may be).
- The computer’s manufacturer or operator doesn’t want to provide it, for reasons including:
- That’s what customers prefer.
- That’s what other governments require.
- Being pro-liberty is the right and moral choice. (Yes, right and wrong do sometimes actually come into play. )
As a general rule, what’s best for any kind of company is — pricing and so on aside — whatever is best or most pleasing for their customers or users. This would suggest that it is in tech companies’ best interest to favor privacy, but there are two important quasi-exceptions: Read more
|Categories: Amazon and its cloud, Google, Microsoft and SQL*Server, Surveillance and privacy, Web analytics||4 Comments|
This year, privacy and surveillance issues have been all over the news. The most important, in my opinion, deal with the tension among:
- Personal privacy.
- General law enforcement.
More precisely, I’d say that those are the most important in Western democracies. The biggest deal worldwide may be China’s movement towards an ever-more-Orwellian surveillance state.
The main examples on my mind — each covered in a companion post — are:
- The Apple/FBI conflict(s) about locked iPhones.
- The NSA’s propensity to share data with civilian law enforcement.
Legislators’ thinking about these issues, at least in the US, seems to be confused but relatively nonpartisan. Support for these assertions includes:
- The recent unanimous passage in the US House of Representatives of a law restricting police access to email.
- An absurd anti-encryption bill proposed in the US Senate.
- The infrequent mention of privacy/surveillance issues in the current election campaign.
I do think we are in for a spate of law- and rule-making, especially in the US. Bounds on the possible outcomes likely include: Read more
- I’ve suggested in the past that multi-data-center capabilities are important for “data sovereignty”/geo-compliance.
- The need for geo-compliance just got a lot stronger, with the abolition of the European Union’s Safe Harbour rule for the US. If you collect data in multiple countries, you should be at least thinking about geo-compliance.
- Cassandra is an established leader in multi-data-center operation.
But when I made that connection and checked in accordingly with my client Patrick McFadin at DataStax, I discovered that I’d been a little confused about how multi-data-center Cassandra works. The basic idea holds water, but the details are not quite what I was envisioning.
The story starts:
- Cassandra groups nodes into logical “data centers” (i.e. token rings).
- As a best practice, each physical data center can contain one or more logical data center, but not vice-versa.
- There are two levels of replication — within a single logical data center, and between logical data centers.
- Replication within a single data center is planned in the usual way, with the principal data center holding a database likely to have a replication factor of 3.
- However, copies of the database held elsewhere may have different replication factors …
- … and can indeed have different replication factors for different parts of the database.
In particular, a remote replication factor for Cassandra can = 0. When that happens, then you have data sitting in one geographical location that is absent from another geographical location; i.e., you can be in compliance with laws forbidding the export of certain data. To be clear (and this contradicts what I previously believed and hence also implied in this blog):
- General multi-data-center operation is not what gives you geo-compliance, because the default case is that the whole database is replicated to each data center.
- Instead, you get that effect by tweaking your specific replication settings.
|Categories: Cassandra, Clustering, DataStax, HBase, NoSQL, Open source, Specific users, Surveillance and privacy||3 Comments|
1. European Union data sovereignty laws have long had a “Safe Harbour” rule stating it was OK to ship data to the US. Per the case Maximilian Schrems v Data Protection Commissioner, this rule is now held to be invalid. Angst has ensued, and rightly so.
The core technical issues are roughly:
- Data is usually in one logical database. Data may be replicated locally, for availability and performance. It may be replicated remotely, for availability, disaster recovery, and performance. But it’s still usually logically in one database.
- Now remote geographic partitioning may be required by law. Some technologies (e.g. Cassandra) support that for a single logical database. Some don’t.
- Even under best circumstances, hosting and administrative costs are likely to be higher when a database is split across more geographies (especially when the count is increased from 1 to 2).
Facebook’s estimate of billions of dollars in added costs is not easy to refute.
My next set of technical thoughts starts: Read more
It is extremely difficult to succeed with SaaS (Software as a Service) and packaged software in the same company. There were a few vendors who seemed to pull it off in the 1970s and 1980s, generally industry-specific application suite vendors. But it’s hard to think of more recent examples — unless you have more confidence than I do in what behemoth software vendors say about their SaaS/”cloud” businesses.
Despite the cautionary evidence, I’m going to argue that SaaS and software can and often should be combined. The “should” part is pretty obvious, with reasons that start:
- Some customers are clearly better off with SaaS. (E.g., for simplicity.)
- Some customers are clearly better off with on-premises software. (E.g., to protect data privacy.)
- On-premises customers want to know they have a path to the cloud.
- Off-premises customers want the possibility of leaving their SaaS vendor’s servers.
- SaaS can be great for testing, learning or otherwise adopting software that will eventually be operated in-house.
- Marketing and sales efforts for SaaS and packaged versions can be synergistic.
- The basic value proposition, competitive differentiation, etc. should be the same, irrespective of delivery details.
- In some cases, SaaS can be the lower cost/lower commitment option, while packaged product can be the high end or upsell.
- An ideal sales force has both inside/low-end and bag-carrying/high-end components.
But the “how” of combining SaaS and traditional software is harder. Let’s review why. Read more
In which I observe that Tim Cook and the EFF, while thankfully on the right track, haven’t gone nearly far enough.
Traditionally, the term “chilling effect” referred specifically to inhibitions on what in the US are regarded as First Amendment rights — the freedoms of speech, the press, and in some cases public assembly. Similarly, when the term “chilling effect” is used in a surveillance/privacy context, it usually refers to the fear that what you write or post online can later be held against you. This concern has been expressed by, among others, Tim Cook of Apple, Laura Poitras, and the Electronic Frontier Foundation, and several research studies have supported the point.
But that’s only part of the story. As I wrote in July, 2013,
… with the new data collection and analytic technologies, pretty much ANY action could have legal or financial consequences. And so, unless something is done, “big data” privacy-invading technologies can have a chilling effect on almost anything you want to do in life.
The reason, in simplest terms, is that your interests could be held against you. For example, models can estimate your future health, your propensity for risky hobbies, or your likelihood of changing your residence, career, or spouse. Any of these insights could be useful to employers or financial services firms, and not in a way that redounds to your benefit. And if you think enterprises (or governments) would never go that far, please consider an argument from the sequel to my first “chilling effects” post: Read more
It’s difficult to project the rate of IT change in health care, because:
- Health care is suffused with technology — IT, medical device and biotech alike — and hence has the potential for rapid change. However, it is also the case that …
- … health care is heavily bureaucratic, political and regulated.
Timing aside, it is clear that health care change will be drastic. The IT part of that starts with vastly comprehensive electronic health records, which will be accessible (in part or whole as the case may be) by patients, care givers, care payers and researchers alike. I expect elements of such records to include:
- The human-generated part of what’s in ordinary paper health records today, but across a patient’s entire lifetime. This of course includes notes created by doctors and other care-givers.
- Large amounts of machine-generated data, including:
- The results of clinical tests. Continued innovation can be expected in testing, for reasons that include:
- Most tests exploit electronic technology. Progress in electronics is intense.
- Biomedical research is itself intense.
- In particular, most research technologies (for example gene sequencing) can be made cheap enough over time to be affordable clinically.
- The output of consumer health-monitoring devices — e.g. Fitbit and its successors. The buzzword here is “quantified self”, but what it boils down to is that every moment of our lives will be measured and recorded.
- The results of clinical tests. Continued innovation can be expected in testing, for reasons that include:
These vastly greater amounts of data cited above will allow for greatly changed analytics.
There are numerous ways that technology, now or in the future, can significantly improve personal safety. Three of the biggest areas of application are or will be:
- Crime prevention.
- Vehicle accident prevention.
- Medical emergency prevention and response.
Implications will be dramatic for numerous industries and government activities, including but not limited to law enforcement, automotive manufacturing, infrastructure/construction, health care and insurance. Further, these technologies create a near-certainty that individuals’ movements and status will be electronically monitored in fine detail. Hence their development and eventual deployment constitutes a ticking clock toward a deadline for society deciding what to do about personal privacy.
Theoretically, humans aren’t the only potential kind of tyrants. Science fiction author Jack Williamson postulated a depressing nanny-technology in With Folded Hands, the idea for which was later borrowed by the humorous Star Trek episode I, Mudd.
Of these three areas, crime prevention is the furthest along; in particular, sidewalk cameras, license plate cameras and internet snooping are widely deployed around the world. So let’s consider the other two.
Vehicle accident prevention
|Categories: Health care, Predictive modeling and advanced analytics, Public policy, Surveillance and privacy||3 Comments|
Most IT innovation these days is focused on machine-generated data (sometimes just called “machine data”), rather than human-generated. So as I find myself in the mood for another survey post, I can’t think of any better idea for a unifying theme.
1. There are many kinds of machine-generated data. Important categories include:
- Web, network and other IT logs.
- Game and mobile app event data.
- CDRs (telecom Call Detail Records).
- “Phone-home” data from large numbers of identical electronic products (for example set-top boxes).
- Sensor network output (for example from a pipeline or other utility network).
- Vehicle telemetry.
- Health care data, in hospitals.
- Digital health data from consumer devices.
- Images from public-safety camera networks.
- Stock tickers (if you regard them as being machine-generated, which I do).
That’s far from a complete list, but if you think about those categories you’ll probably capture most of the issues surrounding other kinds of machine-generated data as well.
2. Technology for better information and analysis is also technology for privacy intrusion. Public awareness of privacy issues is focused in a few areas, mainly: Read more