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content creators are one of the most largest users of tech obtainable. Photographers, videographers, bloggers, vloggers, and everyone in between makes use of the energy of know-how to create their content material. whether it’s Adobe Photoshop, Adobe Lightroom, or any one of the Adobe artistic Suite functions, these all take extra power to run.
energy that most primary consumer workstations simply can’t convey. here is where HP’s new line of Z workstations comes into play. The business is packing the performance content creators want into these workstations to supply content material creators the power they want for a fast and clear workflow.
the brand new workstations are available in two SKUs: the HP Z6 and the HP Z8. each are powered by Intel’s latest Xeon Scalable processors. HP says that designers, statistics scientists and artist workflows will accelerate, enabling more time for new release as information is moved closer to the CPU with the adoption of Intel Optane DC Persistent reminiscence expertise.
HP has also partnered with a couple of companies that content creators are regularly occurring with including AVID, crimson Digital Cinema and NVIDIA to optimize productiveness.
We’ve seen superb success in our excessive-conclusion efficiency class with every of our computer generations perpetually introducing new easy methods to create and be more productive. today, we are pushing the boundaries even additional with the launch of Z6 and Z8 G4, the realm’s first workstations demonstrating the vigour of Intel Xeon and Intel Optane DC, that offer accelerated efficiency with top class processor-architecture, so our valued clientele can create, iterate and put in force quicker than ever before.Xavier Garcia, vp and established supervisor, Z by way of HP, HP Inc.
Intel Optane DC will enable clients to dramatically enhance system efficiency through relocating gigantic datasets nearer to the CPU, plus making it persistent, and in an affordable manner. this could enable for no statistics loss after a power cycle or utility closure. as soon as applications are written to take advantage of this new expertise, workflows can be accelerated as downtime should be a component of the previous.HP Z6 G4 notebook
The HP Z6 G4 pc is a swish powerhouse for disturbing 8k video enhancing in true-time and rendering workloads, outfitted with two subsequent-era Intel Xeon processors proposing as much as 48 complete processor cores in one system, professional NVIDIA and AMD photos and 384 GB of memory.
content material creators can additionally unlock performance for storage-bound applications by means of installation the fast, expert grade storage hardware, with out sacrificing regular PCIe slots. With the pliability to improve over time, the Z6 G4 is designed to grow with the person and gives the potential to scale up in the future with a vast latitude of configurations.HP Z8 G4 notebook
The HP Z8 G4 pc, the strongest workstation on the planet1, powered by way of up to fifty six processing cores and up to 3TB of excessive-pace reminiscence, is in a position to operating complex 3D simulations, disturbing VFX workflows and coping with superior laptop getting to know algorithms. certified for severe utility like ANSYS, Autodesk Flame, and DaVinci resolve, creators can ideate and iterate greater to convey superior work in less time.
With 24 reminiscence slots helping the Z8 G4’s high-pace reminiscence, builders can without problems address huge information units. that includes a latest and complex design, the Z8 G4 presents a call of 1125W, 1450W and 1700W and ninety % efficient vigour components, firing up maximum ranges of processing, memory, pics, storage, and i/O configurability.
HP business Alliances improve productiveness for Creatives
As world creation groups and safety considerations proceed to swiftly raise, so does the need for centralized facilities, international collaboration, and remote working. HP’s far off photos software (RGS), protected with all HP Z Workstations, raises the bar for demanding high-conclusion notebook users like editors and VFX artists by way of enabling remote computer access from any home windows, Linux or Mac equipment, at any place they are. With low latency, optimized visual constancy and rock-strong performance, RGS is the most beneficial far flung-entry and collaboration device.
Recognizing RGS’s knowledge for digital media professionals, Avid is taking part with HP to verify RGS with Media Composer | Cloud VM. initial exams have carried out neatly and a demo of the know-how should be at the NAB show in Las Vegas on the Z by HP sales space (#SL9724). With this far off modifying solution, HP and Avid are offering creators with the performance they are expecting and the pliability they should create excessive fine, speedy turnaround content material from any region.
additionally, working with 8K video is now easier and extra accessible. Updating crimson Digital Cinema’s REDCINE-X professional software to harness NVIDIA’s CUDA technology, crimson will liberate an SDK enabling third parties to develop utility that offloads REDCODE raw decoding and debayering to an NVIDIA GPU. using a single-CPU HP Z4 pc and accelerated through an NVIDIA Quadro RTX 6000 GPU, HP will reveal 8K REDCODE uncooked (8192 x 4320) playback, modifying and color grading in true-time at NAB exhibit 2019.HP Press release
The HP Z6 G4 computer with new Intel Xeon processors is purchasable now for a beginning expense of US$2,372. The HP Z8 G4 workstation with new Intel Xeon processors is purchasable now for a starting expense of $2,981.
What do you think of those new workstations from HP? let us know within the comments beneath or on Twitter, or fb. you could additionally comment on our MeWe web page by becoming a member of the MeWe social network.
The 2019 Porsche 911 Speedster has just been added at the ongoing ny Auto exhibit after the vehicle turned into proven off in theory guise late closing 12 months.
Sitting at the heart of the new Porsche 911 Speedster is a naturally-aspirated four.0-liter flat-six engine which promises 502 hp and 346 lb-feet (469 Nm) of torque whereas revving to an spectacular 9000 rpm. This engine is an identical basic unit outfitted in both the current 911 GT3 and 911 GT3 RS and comprises individual throttle bodies identical to the 911 GT3 R race automobile.
The 911 Speedster could be sold completely with a six-velocity manual transmission which sends vigour throughout the rear wheels. The dash from 0-60 mph (ninety six km/h) can be handled in just three.8 seconds and the German activities vehicle can vigour via to a 192 mph (309 km/h) top speed.
Sitting on the heart of the new Porsche 911 Speedster is a naturally-aspirated four.0-liter flat-six engine which promises 502 hp and 346 lb-feet (469 Nm) of torque whereas revving to an remarkable 9000 rpm. This engine is the same fundamental unit outfitted in both the existing 911 GT3 and 911 GT3 RS and comprises individual throttle bodies identical to the 911 GT3 R race vehicle.
The 911 Speedster might be sold completely with a six-pace manual transmission which sends vigor through the rear wheels. The dash from 0-60 mph (ninety six km/h) should be handled in barely 3.8 seconds and the German sports motor vehicle can vigour through to a 192 mph (309 km/h) top speed.
Porsche concentrated appreciably on ensuring the 911 Speedster is as gentle as feasible right through its construction. as a result, the front bags compartment lid, entrance fenders, and rear decklid are all crafted from carbon fiber. The car additionally comes equipped with Porsche Ceramic Composite Brakes as typical and no aircon is featured. despite the fact, shoppers can opt for air conditioning as a no-charge alternative. All up, the new Speedster tips the scales at 3230 lbs (1465 kg).
while the vehicle does feature the same chassis because the 911 GT3 models, it does consist of chiefly-calibrated rear axle steering and new dynamic engine mounts. a group of Satin Black 20-inch core lock wheels are additionally average.
because the name of the brand new 911 implies, it elements a low-reduce entrance windshield and promises to offer one of the vital finest riding experiences of any current 911. different entertaining styling features of the Speedster encompass low side windows, a manually-operated light-weight material excellent and voluptuous buttresses directly at the back of the driver and passenger seats. unluckily, the car doesn’t function the equal retro-inspired wing mirrors and hood-established fuel cap as ultimate year’s 911 Speedster thought.
Order books for the 2019 Porsche 911 Speedster will open on may additionally 7, 2019 and the vehicle is anticipated to land in U.S. dealerships in late 2019. Alongside asserting the vehicle itself, Porsche Design has unveiled a different timepiece exclusive to Speedster homeowners.
Autonomy, an HP company, these days introduced HP records Protector 7 integration for VMware vCloud Director 5.1, bringing information Protector’s automatic, policy-based insurance policy for cloud environments. VMworld is the focal factor for key partners like HP to announce new solutions that incorporate VMware know-how, and facts Protector 7 is a key part of Autonomy’s records insurance plan suite.
VMware vSphere users can now leverage HP data Protector 7 to deduplicate statistics on the application supply, backup server, or goal equipment and to centrally manage deduplicated data flow across the commercial enterprise for disaster healing.
HP facts Protector gives coverage-primarily based statistics insurance plan that enables corporations to control how frequently they back up their statistics in addition to how much statistics to keep. The solution is also designed to guide multitenant cloud environments that require bendy insurance policy workplace for SLA and different compliance requirements.
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A VPN can easily get overcomplicated with a fistful of features that nobody really needs or uses. Then there are VPNs like SpyOff that keep it as basic as possible. SpyOff’s big promise is that it won’t track your browsing habits, and it claims its service is the best for this because it has no device or bandwidth limits.IDG
SpyOff with an active connection.
Having no bandwidth limits is nothing new and many services offer this, but unlimited devices is a rare feature. That’s a great option if you have more than five devices—the most common restriction in the VPN space.
Note: This review is part of our best VPNs roundup. Go there for details about competing products and how we tested them.Features and services
When you first start SpyOff, you see a single-panel app dominated by an On/Off button in the center reminiscent of ExpressVPN. Below the primary button is a Choose Location button that sends you to a screen where you have 39 country location options. You can either click on the country itself or click the arrow to the left to choose a specific location in a given country. Some country locations only have one option, while others (such as the U.S.) have several.
SpyOff doesn’t offer a favorites option for quick access to your preferred location option. There is, however, a Recent Locations tab where you can choose servers you’ve visited before.
Diving into the settings (accessible via the three-dots menu option in the upper-right corner), you have a few options to tweak. Under VPN Protocol there’s an option to run SpyOff over TOR. You can also select an option under Advanced for “Enhanced DNS leak protection.” That’s a must for this service. In our tests, with DNS leak protection off SpyOff just uses our default DNS servers. That is a big no-no if you want to protect your privacy and be as anonymous as possible online.
SpyOff also offers links to DNS and WebRTC leak tests on SpyOff’s own website. That’s nice, but I’d recommend using third-party tests for this, such as DNSLeakTest and IPLeak.Performance IDG
This error was a common occurrence with SpyOff in our tests.
When it comes to speeds and performance SpyOff was an odd experience. First, there were several server locations I couldn’t log in to with my test account, such as the U.S., UK, and Germany—three of my most common test locations. I asked the company about this, and they couldn’t see any reason why this was happening. I tried using two different machines—including one that was reset to a fresh copy of Windows 10—and each time it wouldn’t connect to the aforementioned locations, among others.
I don’t know if there was just some weirdness with my account, but I have to review the VPN I’ve been handed, and this was a big problem in my testing. If this improves I will retest the VPN and update this review.
As for performance, it was typically around 32 percent of the base speed. That’s pretty average performance and should be acceptable for most everyday uses. One day during testing I did get a very high speed on a Canadian server that hit nearly 60Mbps with a base speed of about 86Mbps.Privacy, anonymity, and trust
SpyOff is officially based in San Marino, a microstate in the north of the Italian peninsula. The team itself appears to be based in multiple locations around the world.
SpyOff has non-traditional pricing, offering 16 months for $90. That works out to about $67.20 for a year. That’s a little higher than the $60-per-year norm, but it’s not too bad. That said, you can find cheaper options.
SpyOff accepts payment via credit cards, PayPal, and cryptocurrencies via BitPay. It also has a 30-day money-back guarantee.Conclusion
SpyOff is okay. It has acceptable speeds, the fact that you can use it with unlimited devices is a big plus, but the weirdness with the login issues was troubling. The fact that the DNS changing didn’t happen automatically in our tests would be an issue for privacy-minded users. You can find cheaper options, albeit with more stringent device limits, and given the lack of added features I would definitely shop around to compare prices.
Editor’s note: Because online services are often iterative, gaining new features and performance improvements over time, this review is subject to change in order to accurately reflect the current state of the service. Any changes to text or our final review verdict will be noted at the top of this article.To comment on this article and other PCWorld content, visit our Facebook page or our Twitter feed.
For organizations, understanding what data they store and analyze is gaining increasing urgency due to new privacy regulations, from the Global Data Privacy Regulation (GDPR) to the California Consumer Privacy Act (CCPA) and Brazil’s General Data Protection Law (LGPD). But these regulations are not the only reason organizations are focused on privacy.
Security imperatives and pressure to extract more value from the information they store has also put pressure on companies to get data privacy right. Historically, organizations have invested in a variety of technologies to inventory their physical assets, such as servers and PCs, but lacked adequate technology to find, map and inventory data assets.
After all, the challenge to transition to becoming a data-driven organization extends beyond the practical considerations of how to automate the data pipeline and map data assets, to answering whether the actions to access, analyze or share data are consistent with compliance, risk and privacy considerations. Balancing the drive toward becoming a data-driven organization, while ensuring privacy-aware data governance has now emerged as a crucial strategic concern. However, traditional data classification and cataloging tools simply lack the capabilities needed to find, map and inventory data assets accurately and efficiently at scale in the new age of GDPR and other privacy regulations.A data registry how to
Enter the modern data registry. By taking a fresh approach to data discovery – focused on creating an inclusive list of what data is kept where and why – organizations can better meet data privacy, protection and governance requirements.
Organizations need to start with the basics. A modern data registry cannot be a data warehouse – you’ll simply be duplicating the data it maps, and introducing limitations in scale. Instead, organizations should build the registry in an index-like map, focusing on five key functionality and operational characteristics:
1. Content granularity: Privacy regulations, like GDPR and CCPA require organizations to account for the data they collect – and this isn’t just knowing the type of data they collect. Companies need to know what data they have and who that data belongs to. Privacy is all about people, so knowing the “people” context of data is essential to meeting privacy requirements.
2. Usage context: Knowing what and whose data you have is a critical first step but creating a modern data registry with complete data intelligence means going further. This requires operational, technical and business knowledge, such as who can access this data, what applications are consuming the data, what third parties have access to the data, what is the purpose for collecting this data and does the organization have adequate consent to collect and process the data.
3. Data source coverage: A data registry that only covers unstructured files or relationship databases will not provide a complete data inventory. With the growing amount of data sources and applications used throughout the enterprise, organizations need to create a process that covers both unstructured file shares and structured databases, big data, cloud, NoSQL, logs, mail, messaging, applications and more.
4. Ability to scale: Organizations gather and analyze tens, if not hundreds, of petabytes of data. With increasing pressure to extract more value from data, this number is only increasing. A modern data registry not only needs to deliver an efficient index of data along with associated usage, but it must do so in a way that is scalable for a global enterprise.
5. Dynamic not static: Once a data registry is created, organizations must anticipate that it will be changed and moved on a constant basis. Consequently, the registry must be able to self-update and accommodate any changes in near real-time to provide the clearest, most accurate picture of what data is kept where, when, and who it belongs to.A new approach to building a data registry from data intelligence
Once the functional and operational foundation for a modern data registry is built, it is time to create a full accounting and inventory of your enterprise’s distributed data assets. This requires data intelligence down to the discrete entity value – something not possible with metadata alone. Obtaining this level of data requires a hybrid approach to content discovery and contextualization, achieved by considering these four key requirements:
1. Entity discovery and resolution: In order to obtain the level of data intelligence necessary for privacy and protection use cases, organizations need a data discovery mechanism that can extract and resolve data entities based on data values – no matter if the data resides in structured, unstructured or semi-structured stores. Organizations also need to implement scanning systems that can disambiguate identical looking data based on context. For example, your system should be able to sperate a social security number from an account ID, even though they both may have the same value.
2. Entry correlation and contextualization: Privacy is about people. Period. To comply with privacy regulations, organizations need to account for their data and show correlation or association of data to a data subject. This must be reflected in a modern data registry. While essential for privacy, this can also provide a new level of understanding around the connectedness of data to high value identities like transactional IDs, account IDs and patent IDs.
3. Entity classification by type and category: The approach to building a modern data registry must move past traditional classification tooling. Modern data registries should have entity-level granularity that requires more refined entity-level classification. If built with artificial intelligence or machine learning, this will expand how data is identified based on heuristics and inferred categorizations.
4. Metadata capture and cataloging: Even though pure-play metadata catalogs leave much to be desired from the registry standpoint, they still provide value because they can record where data categories can be found. This helps to both classify data entities correctly and identify where to prioritize deeper entity searches. The challenge lies in relying on human tags and annotations, since human error makes this data privy to inconsistencies. So, while technical metadata is important, you also need to capture operational and business context like access rights, purpose of use, or consent.
It cannot be said enough – the only way to comply with privacy regulations like GDPR and CCPA is if the organization can account for what data they hold and what individual the data belongs to.
A modern data registry looks beyond simply classifying and cataloging data to show the correlation and association of data to a data subject. Providing a new understanding of the connectedness of data to high-value identities no matter if they are located- in the data center or the cloud.
As a proponent of baseline federal privacy legislation, I am encouraged that proposals that would have been poison pills not long ago, such as individual rights to see, correct and delete data as well as new authority for the Federal Trade Commission, are drawing wide support now. But some crucial and difficult issues remain wide open.
In a recent Brookings paper looking at early draft privacy bills that include some of the proposals, I observed that “thinking about how to address standards for behavior in U.S. legislation—how data is collected, used, and shared—are less evolved than for the individual rights under discussion.” Yet standards for collection, use and sharing of personal information are at the crux of the debate ahead. Such standards will determine how much legislation succeeds in shifting the focus from consumer choice to business behavior and thereby enables individuals to trust that personal information will be protected regardless of what notices they manage to read, pop-up windows they click through or privacy settings they adjust.
Collection is the linchpin, because it defines and can obviate the risks and obligations that come with using and storing data. This post proposes language for a standard on collection. This language is rooted in recognized principles on collection of personal information but adds some general guideposts to inform the application of the principles in the infinitely varying contexts of today’s data-driven world. At the same time, it seeks to allow flexibility for innovative data uses.
The OECD Fair Information Practice Principles, which underlie many privacy laws and frameworks in the U.S., Europe and elsewhere, articulate a “collection limitation principle.” This principle holds that “there should be limits to the collection of personal data” but, other than saying that “any such data should be obtained by lawful and fair means,” does not frame what these limits should be. Some constraint is implied in the “purpose specification principle”: The affirmation that “the purposes for which personal data are collected should be specified not later than at the time of data collection” suggests collection must have some definable purpose.
When the OECD principles were adopted in 1980, collection was limited by available technology. Databases had specified fields and analog information had to be entered in order to digitize it, and limits on computing power and data storage narrowed the choices of what data to collect and process. Now these limits have been obliterated. Instead of putting some constraint on collection, technology enables virtually limitless collection.
The OECD declaration that “there should be limits to the collection of personal data” seems self-evident. It should not be permissible to collect anything and everything available. Some notorious privacy failures have come from people collecting information simply because they could: Google Street View mappers collecting content streams from unprotected Wi-Fi hotspots along their way, the Brightest Flashlight smartphone app collecting location data, Uber employees using the company’s “God View” to identify users taking morning-after “rides of shame,” and Cambridge Analytica leveraging research access to scoop up data on millions more Facebook users.
Hence the need for some contours to the general principle. In addition, any limit on collection needs to be untethered from purpose specification. This principle has become conflated with notice and, in turn, with consumer choice and consent. As a result, privacy policies tend to specify every form of data a company might conceivably want to collect and expansive catalogs of its uses because this provides legal protection. The purpose specification contained in these notices, however, does not define in a considered way what data the entity actually needs. Collection limits need to be independent of whatever forms of notice are provided to individuals, and not self-defined.
The European Union’s General Data Protection Regulation (GDPR) deals with this by providing that processing of personal information is “lawful” only if it fits within enumerated grounds. These primarily include consent, performance of a contract, legal obligations and the “legitimate interest” of the entity responsible for the processing. All these grounds are carefully circumscribed, with legitimate interest balanced by the interests and rights of the “data subject,” the person whom the data relates to.
Although U.S. privacy legislation should encompass the GDPR’s enumerated grounds, it should not follow the EU’s approach in prescribing the exclusive grounds for collection. This approach is rooted in a civil law system that generally aims to govern new development in advance by laying out systematic and comprehensive rules. That differs from the way we think under Anglo-American common law.
An American privacy law should take an approach more consistent with the post hoc, iterative system of the common law. As I reminded Commerce Department staff during the drafting of legislation based on the Obama administration’s 2012 Consumer Privacy Bill of Rights, most tort law and much other law rests on judgments about what is reasonable under the circumstances, the entire body of U.S. competition law is founded on two sentences of Sections 1 and 2 of the Sherman Act, and most constitutional law on particular clauses. I believe it is not entirely coincidental that this iterative approach is also how today’s software-driven technology operates as it develops through versions, updates and patches. A broad standard can allow flexibility for unanticipated data uses and exploration without permitting unbounded collection.
The language that follows is drafted with these considerations in mind:
Collection and processing [defined terms] of personal data shall have a reasonable, articulated basis that takes into account reasonable business needs of the [covered entity/controller/etc.] engaged in the collection balanced with the intrusion on the privacy and the interests of persons whom the data relates to.
This language is deliberately not detailed, but it is tethered to various strands of law and thought about privacy and other areas. It contains four elements that bound data collection: (1) a reasonable articulated basis, (2) reasonable business needs of the collector, (3) impact on privacy and (4) impact on other interests of individuals.
The practical effect of these elements is illustrated by privacy failures mentioned above. The Street View collection was simply a case of engineers in the field happening on data streams from unencrypted Wi-Fi hotspots and scooping them up simply because they were available and might be interesting someday. In other words, it was done with no specific purpose in mind—for no reasonable, articulated basis. The Brightest Flashlight app collected and shared location data continuously, even though location is irrelevant to the functioning of a flashlight. In short, there was no business need for the location (this posits that collecting data purely for the sake of exploiting it unilaterally is not a reasonable business purpose). And while Uber’s abuse of its “God View” fails on all elements—it was mindless and not for any business purpose—it is most notorious for its intrusion on private behavior in malicious ways users could not expect. Cambridge Analytica is a more complex matter that I’ll come back to after discussing the antecedents of the proposed standard.
The proposed standard is an expression of the collection limitation principle as reframed in the White House Consumer Privacy Bill of Rights as “focused collection,” articulated as “a right to reasonable limits on the personal data that companies collect and retain.” The standard outlines contours for these reasonable limits and, by invoking the privacy and other interests of affected individuals, adds meaning to what the OECDOECD called “fair means.” The reasonable, articulated basis element resembles the explanation of the 2012 focused collection principle as holding “that companies should engage in considered decisions about the kinds of data they need to collect to accomplish specific purposes.” The focused collection principle makes data minimization not an absolute but calls on companies to make considered judgments about what data they really need and why they need it. To put it in trending terms—and in contrast to the examples of mindless collection above—the reasonable, articulated basis calls for mindfulness about collection.
This element also harks back to the origins of the repurpose specification principle. By demanding considered choices about what data to collect, it provides an accountability check that need not be connected to what is described in privacy policies or other notices to the public. The focus is on the collection decision itself, not on the disclosure.
The proposal uses the word “reasonable” twice. It is an elastic term but, as mentioned above, there are broad bodies of law that involve judgments of reasonableness—reasonable care, the reasonable person and reasonable fear of harm are commonplace examples—and lawyers and judges are trained to make these judgments. Privacy is not a green field in this respect. William Prosser long ago distilled 70 years of law applying the famous Warren and Brandeis law review article on the right to privacy into the Restatement (Second) of Torts, which defined intrusions on seclusion and private life in terms of what would be “highly offensive … to the reasonable person.” In significant respects what Daniel Solove and Woodrow Hartzog have characterized as the FTC’s “common law of privacy” has defined unfair and deceptive practices in terms of reasonable practices and industry standards; in the high-profile Wyndham Hotels decision, the Third Circuit Court of Appeals held that the FTC’s authority to regulate unfair cybersecurity practices encompasses “cases where a business[’s] … failure to employ reasonable security measures” causes injury to consumers. The Securities and Exchange Commission has followed a similar path in its monitoring of cybersecurity.
There is a growing body of professional standards and practice to inform these judgments. The American Bar Association has recognized a new legal specialty in privacy law, and the International Association of Privacy Professionals now comprises more than 40,000 members worldwide. Numerous other organizations have issued manuals and audit standards on privacy and security practices. But no matter how specific a privacy law, uncertainty will be inherent. The GDPR is more detailed than a great many people would advocate for the U.S., but it has required much explanatory guidance from the European Data Protection Board, the collective body of EU data protection regulators.
The specific language “reasonable, articulated basis” is adapted from existing law protecting privacy interests. The USA FREEDOM Act was adopted in 2015 to constrain federal bulk collection of telephone metadata within the U.S. It requires the government to apply to the Foreign Intelligence Surveillance Court for a warrant to analyze metadata; the warrant application must include a specific selection term and facts showing the term is relevant to an investigation and “a reasonable, articulable suspicion” that the term is associated with a foreign power engaged in international terrorism (which provides the lawful basis for government surveillance).
This standard codifies what the National Security Agency (NSA) used as a standard for the basis on which an analyst would be permitted to query the metadata that the agency collected in bulk from U.S. telephone carriers under Section 215 of the USA PATRIOT Act, which the USA FREEDOM Act replaced. The NSA’s standard was based on the formulation distilled from the Supreme Court’s 1968 decision in Terry v. Ohio, in which the court held that a stop-and-frisk was reasonable under the Fourth Amendment where the police officer was deemed to have a reasonable suspicion based on observation and experience that the suspects were armed and dangerous.
The “reasonable, articulated basis” language therefore has a connection to ways we have protected our conceptions of privacy as a constitutional right. Laws on government access focus on the reasons for obtaining information and the use of information in connection with those reasons. Why not do the same for access to information by other institutions, especially since the time has long passed since we have had much choice about such access? The USA FREEDOM language also has the tactical value of having passed Congress before—and within recent memory at that.
The use of “reasonable business interests” resembles the “legitimate interest” grounds in the GDPR, one of the more subtle provisions of that regulation. Like legitimate interest, it both validates business interests as a reason for collection but (especially in conjunction with the “reasonable, articulated basis” language) judges these interests with an objective standard, and also requires a balancing test. In turn, this balancing against individual interests seeks to promote the data stewardship many companies profess and obligates them to protect the interests of individual subjects of data. ThisThis gets at the “duty of loyalty” in the Data Care Act proposed by Democratic Sen. Brian Schatz of Hawaii, one of the “working group” of Senate commerce committee leaders drafting legislation, and the “golden rule” I proposed last July “that companies should put the interests of the people whom data is about ahead of their own.”
Looking at privacy intrusion for purposes of this balancing also brings in privacy risk assessment, something that is basic to data management. Privacy or information security analysis begins by looking at what data is collected and how it flows, and then assessing the risks associated with this data, its uses and its movement. Privacy risk assessments are called for in the GDPR, a made-in-America idea developed and deployed in federal government agencies. One of the most effective aspects of the bill debated in the Washington state legislature is a section requiring privacy risk assessments, including whether “potential risks to the rights of the consumer outweigh the interests of the controller, consumer, other stakeholders, and the public in processing the personal data of the consumer.” This gets at the same thing in a less detailed way.
The language does not answer every question that can arise concerning collection of personal information. Neither collection limits nor any other single provision can carry the full weight of addressing business conduct on handling personal data.
A successful privacy statute will have to work holistically, and interpretation of rules on use will have to work holistically to shape the boundaries of a collection standard. For example, it may take provisions or rulemaking that exclude certain sensitive data fields or targeting to establish boundaries for behavioral advertising. But, even if behavioral advertising in general is considered a reasonable business purpose, this collection language could be construed as barring Target’s processing of purchasing data to deliver ads for maternity products to a secretly pregnant teenager as an excessive intrusion on her privacy and interests.
This standard might also reach Cambridge Analytica’s “harvesting” of data from the contacts of its original paid research subjects., but I concede it is not clear-cut. As Danny Weitzner pointed out in Lawfare last year, that case is really about the secondary use of data for purposes outside the context of the research subjects original consents and expectations.
Nor should a particular standard attempt to answer every question. The greater volume, velocity and variety of data today mean a greater volume, velocity and variety of challenges in managing data responsibly. There are trade-offs between certainty and creativity, between precision and flexibility; the issues are too diverse for one size to fit all. So successful privacy legislation should avoid a checklist for compliance and instead be flexible, adaptable and focused on outcomes. This is what the standard above aims to do by putting a check on unthinking collection and putting a focus on the impact on privacy and the individuals who can be linked to the data collected.
 “Collection” probably needs to be defined to be clear that it is not limited to online collection.
 What to call entities covered by statute varies with proposals. I favor adopting the controller/processor distinction used in EU law and the Washington state bill, but that’s a subject for another day. So is what to call the individuals protected, because I consider using a possessive in this context to be confusing.
 A rule of construction may be helpful to guide the meaning of “risks to privacy” and “interests of persons that the data relates to.”
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