United States: DOJ and FTC focus on data abuse in price-fixing and monopoly cases

Introduction

‘Today’s currency is data.’^[1] And companies collect a staggering amount of it. Walmart, for example, claims that it instantly solves customer issues by processing up to 40 petabytes of recent transactional data – that is twice as much data as the US Library of Congress holds in its entire digital collection.^[2]

Those troves of data are valuable to both companies and their customers. Companies use data to target their advertisements, improve their products, innovate and increase their efficiency. Those efforts can benefit consumers by better matching supply and demand, and by expanding the reach of markets to bring together more sellers and buyers.

Large-scale data collection also fuels innovation. Many new machine learning and artificial intelligence (AI) products require massive data sets to train. And free-to-consumer products such as Facebook (Meta) and Google Search are free to consumers because the companies collect and commercialise troves of consumer data.

As data has proliferated, it has become an important element of competition in the digital economy – and thus a target of antitrust scrutiny. Early signs from the Trump administration suggest those efforts will be vigorous. Even before the inauguration, Vice President J D Vance asserted that Alphabet (Google’s parent company) should ‘[a]bsolutely’ be broken up.^[3] In March 2025, Acting Assistant Attorney General (AAG) Omeed Assefi approved robust proposed measures aimed at remedying Google’s search monopoly, including requiring the company to divest its Chrome web browser.^[4] Contextualising these recommendations, AAG Assefi explained that the Department of Justice (DOJ) Antitrust Division would be ‘aggressive in its focus on American workers, consumers and businesses and resist efforts by big companies to hold sway over enforcement’.^[5] Further, the confirmation in March 2025 of Gail Slater as head of the Antitrust Division underscores the administration’s assertive approach; as President Trump remarked when announcing Slater’s nomination, ‘Big Tech has run wild for years, stifling competition in our most innovative sector.’^[6]

This chapter details how US antitrust enforcers and courts view uses and abuses of data. It begins with a discussion of data’s role in monopolisation cases, such as the high-profile actions against Google, Facebook and Amazon. It next addresses how enforcers treat mergers in which the merging parties maintain competitively valuable, or competitively sensitive, data. It then turns to issues of collusion, particularly how enforcers view new technologies such as AI in cases involving information exchanges or alleged price-fixing. The chapter concludes with a discussion of how data privacy has emerged as a new metric of market power, an element of competitive harm and potential procompetitive justification for otherwise anticompetitive conduct.

Data and monopoly

Companies collect data because they believe it will give them an edge. Of course, collecting and analysing a large amount of data is familiar in many industries and does not by itself pose an antitrust issue.^[7] But enforcers have taken action when firms have used control over data to create or maintain monopoly power, particularly in markets with significant network effects.

Data as a tool of monopoly power

Government enforcers have brought monopolisation challenges based in part on theories of data-related harm. In three high-profile cases, enforcers charged alleged monopolists with withholding or degrading valuable data to entrench their monopoly position. In the first, against Facebook, the Federal Trade Commission (FTC) alleged that Facebook cut off rivals’ access to its platform and its user data. But the district court largely blessed this conduct under Verizon Communications Inc v. Law Offices of Curtis V Trinko (Trinko), which provides that a monopolist generally has no duty to give rivals a helping hand.^[8] In two government cases against Google (one filed by the DOJ and 17 states and the other by a Texas-led group of 17 states), it was alleged that Google abused its monopoly over digital advertising technology to make it more difficult for customers to use its competitors’ advertising tools. Google also tried to characterise this conduct as a permissible refusal to deal under Trinko, with less success. The cases are ongoing but, in the early stages, these disparate results illustrate that although monopolists have no duty to affirmatively assist their rivals, those who negatively interfere with their rivals may violate the antitrust laws.

Facebook

In December 2020, the FTC and state attorneys general sued Facebook for allegedly monopolising the market for personal social network services.^[9] The case has since narrowed to focus on the FTC’s challenge to Facebook’s acquisitions of WhatsApp and Instagram^[10] – and trial on that challenge began on 14 April 2025^[11] – although, at first, the complaint also challenged Facebook’s alleged efforts to restrict competitors’ access to its data.^[12]

Facebook allegedly has ‘a rich set of data about users’ activities, interests, and affiliations’^[13] and it shares some of that data with third-party application (app) developers, who integrate it into their own apps. But Facebook allegedly restricted access to that data to only those developers whose ‘apps neither competed with Facebook . . . nor promoted competitors’.^[14] This, according to the complaint, deterred developers from ‘working with other platforms that compete with Facebook’.^[15]

The district court largely rejected these allegations under Trinko’s no-duty-to-deal rule.^[16] The court first laid out three antitrust policy reasons for this rule: that requiring a monopolist to aid its rivals would (1) undermine the monopolist’s incentive to innovate, (2) put the court in the unwelcome position of central planner and (3) open opportunities for collusion between monopolist and rival.^[17] ‘Applying these principles’, the court held that ‘Facebook’s general policy of withholding [data] from competitors . . . was plainly lawful’.^[18]

Google Ads

In two cases, filed in December 2020 and January 2023, the DOJ and two groups of state attorneys general sued Google for monopolising digital advertising markets.^[19] The complaints allege that Google is ‘the dominant player on both sides of the digital advertising industry’: it controls the sell-side for more than 90 per cent of publisher inventory, controls the buy-side for a large fraction of advertisers seeking to buy display ads, and controls the auction in the middle where most display advertising inventory is sold.^[20] The government cases do not allege that Google failed to aid its rivals, as Facebook allegedly did; according to the DOJ complaint, the company ‘force[s] publishers and advertisers to use its products while disrupting their ability to use competing products effectively’.^[21]

In denying Google’s motion to dismiss the case in Texas v. Google, the district court held that Texas had properly alleged an antitrust tying claim, by which Google uses its must-have advertising demand to force publishers to use its advertising server.^[22] Google then used its control over publisher advertising servers to manipulate which advertisers bought the publisher inventory and at what price; for example, Google’s Project Bernanke:

would underpay a publisher after a transaction cleared on AdX, with Google secretly retaining a portion of the winning bidder’s payment. Google then added this secretly retained payment into a pool that was used to increase the bids of Google’s advertiser clients using AdX. As described in the Complaint, the end result was to boost advertisers’ bids on AdX, ensuring that the transaction cleared on AdX and not a rival exchange.^[23]

Similarly, Google diverted ‘the most high-value inventory of publishers exclusively to [its ad exchange], which had the effect of “starving” rival exchanges of scale and liquidity’.^[24]

Google also moved to dismiss the DOJ’s antitrust complaint and again the district court denied the motion. The district court drew the same distinction between ‘trying to encourage innovation and reward[ing] people and companies that are able to come up with new ways of doing things’ versus ‘stifling innovation and competition’ from rivals.^[25] ‘[A]t certain points, some companies may get too big for their own good, . . . . [and] the technology may become so dominant that it’s just crushing all other elements where there can be innovation.’^[26]

Data and network effects

US enforcers have also targeted efforts by monopolists to rob rivals of scale. In recent actions against Google and Amazon, enforcers have alleged that those companies foreclosed more efficient rivals by depriving them of scale and the benefits of data-fuelled network effects.

Network effects make products or platforms more valuable the more people use them. Uber’s value to riders stems from its many drivers, and its value to drivers stems from its many riders. As the network grows, it gains in value as each new connection facilitates more connections – more riders means more potential customers for the drivers, and more drivers means shorter waiting times for the riders.

These network effects benefit consumers, but as former AAG Jonathan Kanter said in 2023, they may ‘make monopoly power even more durable and harmful’.^[27] Dennis Carlton and Ken Heyer, both former Deputy AAGs for Economic Analysis, have explained a related phenomenon: ‘Where scale economies are important, the more business a firm takes from its rivals, for whatever combination of reasons, the more it weakens the rivals’ ability to exercise a constraining influence on price by threatening the rival’s survival.’^[28] Similarly, in recent actions, enforcers have pointed to actions by alleged monopolists that, when combined with network effects from large-scale data collection, have led to runaway growth unconstrained by competition.

Google Search

In October 2020, the DOJ and a number of state attorneys general brought a monopolisation claim against Google alleging that the company uses ‘exclusionary agreements’ to maintain dominance over online search services and search advertising.^[29] Those exclusive agreements make Google the default search engine on many cellphones, computers and internet browsers. That default status, the complaint alleges, gives Google scale, and Google uses that scale to collect consumer data, which the company in turn uses to ‘deliver more relevant results’ to searchers – each search teaches Google’s algorithms ‘which organic results and ads best respond to user queries’.^[30] Having been deprived of access to similar scale and data, according to the complaint, rivals lack the ability ‘to compete effectively’.^[31]

After a 10-week bench trial, Judge Amit Mehta of the US District Court for the District of Columbia ruled that ‘Google is a monopolist, and it has acted as one to maintain its monopoly’.^[32] Judge Mehta highlighted how Google’s ‘anticompetitive’ exclusive agreements ‘deny rivals access to user queries, or scale, needed to effectively compete’.^[33] Google has access to data ‘that its rivals cannot match’, putting those rivals ‘at a persistent competitive disadvantage’.^[34] For this and other reasons, Judge Mehta concluded that ‘Google has violated Section 2 of the Sherman Act’.^[35]

The DOJ has proposed remedies that aimed to ‘erode Google’s unlawfully gained scale advantages’.^[36] If the proposed remedies were adopted, Google would have to, among other things:

• ‘make critical portions of its search index’ – a library of ‘key signals’ from ‘hundreds of billions of web pages’^[37] – ‘available at marginal cost, and on an ongoing basis, to rivals and potential rivals’;^[38]
• ‘syndicate’ its search results and ranking system to smaller search engines, which would let other search engines ‘rent’ Google’s search technology;^[39]
• provide ‘both user-side and ads data’ to rivals and ‘crawling data rights (such as the ability to opt out of having their content crawled for the index or training of large language models)’ ‘to publishers, websites and content creators’;^[40] and
• notify the DOJ before acquiring or partnering with any company that owns a generative AI product (a more permissive remedy than the DOJ’s initial proposal that Google be prohibited from investing in any AI product that could compete with Google Search).^[41]

The court is holding a hearing on these proposed remedies, which began in April 2025.

Amazon

The FTC and state attorneys general point to a similar data-driven feedback loop in their monopolisation case against Amazon.^[42] The complaint alleges that Amazon has monopolised the online superstore market and the online marketplace services market through an ‘illegal course of exclusionary conduct [that] Amazon deploys to block competition, stunt rivals’ growth, and cement its dominance’.^[43] It further alleges intentional conduct by Amazon, such as anti-discounting^[44] and other allegedly coercive tactics. Further, as evidence of Amazon’s alleged monopoly power, the complaint highlights network effects from the company’s access to ‘valuable shopper data’, which allegedly buttress Amazon’s position ‘in the online superstore market’, creating a barrier to entry for potential competitors.^[45] The complaint alleges that Amazon uses that shopper data ‘to tailor and personalize shopping experiences’ – the company ‘records information about the items a shopper searches for, views, places in their cart, and pays for’ and uses that data ‘to streamline a shopping experience and target specific products to certain customers’.^[46] This data thus, allegedly, ‘power[s] what Amazon calls its ‘flywheel’, the ‘accelerated growth and momentum that network effects and scale economies can fuel’.^[47] The complaint further alleges that competitors who lack a similar ‘critical mass’ of shoppers and shopper data are less able to compete.^[48]

Data exchanges among competitors

Antitrust enforcers have long targeted exchanges of competitively sensitive information among competitors. During a speech in February 2023, former Principal Deputy AAG Doha Mekki explained that ‘exchange of competitively sensitive information can subvert the competitive process and harm competition’ or even ‘facilitate full-blown criminal conspiracies’.^[49] These concerns are just as strong when the competitively sensitive information being exchanged is data. What matters is ‘the structure of the industry involved and the nature of the information exchanged’.^[50] When data is stale, aggregated and public, courts generally view it as less competitively sensitive; the opposite is true when the data is current, specific and non-public.^[51]

Many types of data may be competitively sensitive and, thus, a target of enforcer scrutiny. This includes obvious culprits such as data on prices, costs and output, but also less obvious ones such as data on employee wages, salaries and benefits, as companies compete in the employment market, not just in the market for products and services. In July 2022, for example, the DOJ entered into an US$85 million consent decree arising out of an alleged conspiracy to suppress worker pay at poultry plants.^[52] The consent decree resolved allegations that poultry processors and a data firm exchanged competitively sensitive wage and benefit data.^[53] The defendants agreed not to keep exchanging such data.

For decades, companies relied on guidance documents from the DOJ and the FTC that set out ‘safety zones’ for information exchanges.^[54] Those jointly issued documents clarified types of information exchanges the enforcers viewed as generally unlikely to raise competition concerns. Those less-concerning exchanges included ones in which (1) a third party manages the data collection, (2) the shared information is more than three months old and (3) the data is aggregated and anonymised.

The DOJ withdrew that long-standing guidance in February 2023, noting simply that ‘the statements are overly permissive on certain subjects, such as information sharing, and no longer serve their intended purpose of providing encompassing guidance to the public’.^[55] Principal Deputy AAG Doha Mekki elaborated in public remarks, noting that the guidance had been ‘written at a time when information was shared in manila envelopes and through fax machines. Today, data is shared, analyzed, and used in ways that would be unrecognizable decades ago’.^[56]

One aspect of the DOJ’s shift in approach is an increased scepticism of data exchanges through intermediaries. Deputy AAG Mekki highlighted that the DOJ no longer views ‘the use of a third-party intermediary to facilitate information exchanges’ as evidence ‘that the exchange will not harm competition’.^[57] On the contrary, Deputy AAG Mekki remarked that in some cases, ‘data intermediaries can enhance—rather than reduce—anticompetitive effects’.^[58]

The DOJ brought this new scepticism to a civil case against Agri Stats, Inc, a data intermediary for meat processors.^[59] The complaint alleges that Agri Stats ‘recruited and enabled all major U.S. chicken, pork, and turkey processors to exchange competitively sensitive information through its exclusive subscription and consulting business’.^[60] That sensitive information allegedly includes ‘price, output, and cost data’.^[61] Although Agri Stats anonymises this data, the complaint alleges that ‘processors can and do deanonymize the data, linking particular data to individual processors and processor facilities’.^[62] In private litigation against broiler-chicken producers and Agri Stats, the court confronted similar charges but granted Agri Stats’ motion for summary judgment, holding that there was not ‘sufficient evidence . . . that Agri Stats agreed with the producer defendants to restrict supply and increase the price of’ chicken.^[63] ‘Just because Agri Stats provided a convenient form to transmit the information’, the court observed, ‘does not mean that Agri Stats itself joined the conspiracy’.^[64] Agri Stats cited this ruling in its motion to dismiss the DOJ’s complaint, which the district court permitted to proceed past the pleading stage.^[65]

Private litigants have tracked the DOJ’s increased scrutiny of data intermediaries. In a case against luxury hotel operators, for example, the complaint also names Smith Travel Research, an intermediary that allegedly ‘enables the[ luxury hotel operators] to exchange competitively sensitive information with each other’.^[66] Citing Deputy AAG Mekki’s speech, the complaint alleges that ‘the information exchanges orchestrated by’ Smith Travel Research are ‘highly likely to produce anticompetitive effects’ because, among other reasons, they include current or forward-looking price and supply data.^[67]

Data and price-setting algorithms

In her speech, Deputy AAG Mekki also highlighted new technologies that might make previously benign data exchanges problematic: ‘In some industries, high-speed, complex algorithms can ingest massive quantities of “stale,” “aggregated” data from buyers and sellers to glean insights about the strategies of a competitor’.^[68] Data analysis at scale may erode ‘the distinctions between past and current or aggregated versus disaggregated data’.^[69]

Companies also use high-speed, complex algorithms to set ‘prices for everything from laundry detergent to bowling lane reservations’.^[70] This use of algorithms is a recent phenomenon but a former acting chair of the FTC, Maureen Ohlhausen, said that it ‘raises familiar issues that are well within the existing canon’ of antitrust law.^[71] She laid out a simple test to evaluate those issues: ‘If it isn’t ok for a guy named Bob to do it, then it probably isn’t ok for an algorithm to do it either.’^[72] That guy-named-Bob test has gained some purchase among courts,^[73] enforcers^[74] and private litigants.^[75] And although the Trump administration has signalled that it will avoid ‘an overly precautionary regulatory regime’ for AI technology,^[76] there are no signs that it has changed position on algorithmic price setting that emulates ordinary human price-fixing.

Enforcers have expressed concern that price-setting algorithms may facilitate collusion and price-fixing, potentially ‘resulting in higher prices, or at a minimum, a softening of competition’.^[77] That concern arises particularly when ‘competitors adopt the same pricing algorithms’^[78] and those algorithms incorporate non-public data into their pricing recommendations.^[79] The question that situation raises, put in guy-named-Bob terms, is: ‘Is it ok for a guy named Bob to collect confidential price strategy information from all the participants in a market, and then tell everybody how they should price?’^[80]

Enforcers have answered ‘no’. In 2024, the DOJ and eight states brought a civil action against RealPage Inc, which operates algorithmic pricing software for rental apartments.^[81] The complaint alleges that RealPage gets competing landlords to share non-public, competitively sensitive information about their apartment rental rates and other lease terms to train and run RealPage’s software.^[82] The software then generates recommendations, including on apartment rental pricing and other terms, for participating landlords based on their and their rivals’ competitively sensitive information.^[83]

The DOJ views this type of exchange as per se unlawful. In a statement of interest in the parallel multidistrict litigation, In re RealPage, Inc, the DOJ described RealPage’s conduct as algorithmic price-fixing.^[84] Algorithmic price-fixing occurs, according to the DOJ, when competitors (1) share their competitive information, such as non-public pricing and inventory data, with a common software algorithm and (2) rely on pricing decisions from that common algorithm, while (3) knowing that the algorithm analyses information from multiple competitors.^[85] Applying this rubric, the DOJ said that the RealPage plaintiffs state a per se price-fixing claim because they adequately allege that the competing landlords ‘knowingly combine their sensitive, nonpublic pricing and supply information in an algorithm that they rely upon in making pricing decisions, with the knowledge and expectation that other competitors will do the same’.^[86] In a later statement of interest, the DOJ took this reasoning one step further, stating that ‘an agreement among competitors to fix the starting point of pricing is per se unlawful, no matter what prices the competitors ultimately charge’.^[87] So, according to the DOJ, even if competitors do not always ‘adhere to pricing recommendations’ from algorithms, a joint agreement to use algorithmic pricing is ‘per se unlawful’.^[88]

The few algorithmic price-fixing court decisions out so far suggest that, to state a claim, a plaintiff must allege that the algorithm at issue uses non-public data to set prices. That was the court’s conclusion in Gibson v. MGM Resorts International, which involved allegations of price-fixing in the market for Las Vegas casino hotel rooms.^[89] The court concluded that a successful price-fixing conspiracy claim ‘based on the use of algorithmic pricing depends in part on the exchange of nonpublic information between competitors through the algorithm’.^[90] The plaintiffs there failed to allege that the algorithm used any non-public information, so the court held they failed to state a claim.^[91] The RealPage plaintiffs, by contrast, stated a claim by alleging that RealPage ‘inputs a melting pot of confidential competitor information through its algorithm and spits out price recommendations based on that private competitor data’.^[92]

Data and merger analysis

Data also figures prominently in merger reviews, particularly in the past decade. Enforcers have sought to block mergers that would give the merged entity dominant control over a data market and the ability to deprive rivals of important data sources, or control over competitively sensitive data.

Mergers that increase data concentration

Enforcers may challenge mergers that would create an entity with a dominant share of a market’s data. In one such case, the FTC challenged Dun & Bradstreet’s 2009 acquisition of Quality Education Data.^[93] The FTC alleged that the combined entity controlled more than 90 per cent of all K-12^[94] educational marketing data, which companies use to sell books and other educational materials to teachers.^[95] As part of a consent decree, the FTC required Dun & Bradstreet to divest certain assets to a competitor and to sell that competitor an updated K-12 database.^[96]

Mergers that give companies control over key data inputs

Enforcers also may challenge mergers that would enable a merged firm to deny rivals important data inputs. In 2015, for example, the DOJ challenged Cox Automotive’s proposed acquisition of Dealertrack Technologies.^[97] The DOJ was concerned that Cox would become an effective monopolist in the market for inventory management solutions – technology that helps car dealers manage and price their new-vehicle inventories. But the DOJ also noted that the acquisition would give Cox control over vehicle information data that is a key input for inventory management. With control over that data, the DOJ alleged, Cox could ‘deny or restrict access’ to the data ‘and thereby unilaterally undermine the competitive viability of Cox’s remaining [inventory-management] competitors’.^[98] To allow the deal to go through, the DOJ required (1) Cox to divest part of Dealertrack’s business and (2) both parties to keep sharing their valuable data with inventory-management companies on the same terms as before the merger.^[99]

Mergers involving platforms may raise similar data-control issues. In new guidelines published in 2023, the FTC and the DOJ note that they may challenge mergers that would allow platform operators to deny a rival the benefits of key data.^[100] They give as an example a merger in which a platform would acquire ‘data that helps facilitate matching, sorting, or prediction services’, which the platform could use ‘to weaken rival platforms by denying them that data’.^[101]

The FTC’s challenge in 2023 to IQVIA’s acquisition of Propel Media fits that rubric.^[102] IQVIA maintains data about healthcare providers and their prescribing behaviour. The FTC describes that data as the ‘gold standard’ in the industry.^[103] The acquisition, according to the complaint, would also give IQVIA a dominant position over ‘demand-side platforms’ – platforms on which healthcare companies buy advertising space. So the FTC alleged that the acquisition would give IQVIA ‘the ability and incentive to leverage its control’ over its important data sets ‘to foreclose or otherwise disadvantage current or emerging rivals’ to its demand-side platforms, which use IQVIA data as an input, ‘raising prices for its data, reducing data quality, or restricting advertisers from using its data’.^[104] After the FTC secured a preliminary injunction, the parties abandoned the acquisition. The FTC did allege that competing advertising platforms were using IQVIA’s data and might not be able to continue to do so post-merger.^[105]

Mergers that give companies access to sensitive data

Enforcers have also targeted mergers that would give companies access to competitively sensitive information about their rivals; for example, in February 2022, the DOJ and two states sued to prevent UnitedHealth from acquiring Change Healthcare, citing such concerns.^[106] Change provides technologies that healthcare providers use to submit claims and that insurers use to assess those claims.^[107] In doing so, the complaint alleged, Change gains ‘access to vast amounts of competitively sensitive data about United’s rivals—data that reveals how their plans are designed and how they calculate payments to providers, for example’.^[108] The key anticompetitive effect of the merger, the complaint alleged, was that it would make United privy to that sensitive data, which United allegedly could use ‘to co-opt its rival insurers’ innovations and their competitive strategies’.^[109] After a trial at which executives at competing insurers testified that the deal would not stifle innovation, the judge ruled against the DOJ in September 2022 and the DOJ dropped its appeal in March 2023.

The DOJ raised similar concerns in its November 2020 challenge to Visa’s acquisition of Plaid.^[110] Much of the complaint focused on how the acquisition would allegedly have allowed Visa to ‘eliminate a nascent competitive threat’ in Plaid,^[111] but the complaint also highlighted Plaid’s extensive access to ‘important financial data from over 11,000 banks’.^[112] According to the complaint, ‘[a]cquiring Plaid would also give Visa access to Plaid’s enormous trove of consumer data, including real-time sensitive information about merchants and Visa’s rivals’.^[113] Visa allegedly could have used this data to ‘further raise barriers to entry and expansion’ by rivals.^[114] The companies abandoned the merger before trial.

Data as a merger-specific efficiency

Data is not invariably a negative in merger analysis; for example, at trial over its vertical merger with Time Warner, AT&T argued that the merger would yield data-related benefits to consumers. Specifically, it argued that AT&T’s consumer data would increase the value of Time Warner’s advertisements and that reduced ‘bargaining friction’ over access to that data would reduce costs.^[115] The district court concluded that these quality improvements and lower costs would ‘inure right away to the benefit of AT&T’s current video distribution subscribers’.^[116] So at least in the vertical-merger context, courts may view data-related efficiencies as benefits to consumer welfare.

Data privacy and security

Enforcers traditionally sought to prosecute privacy violations under consumer protection laws but companies now compete based on how they approach data privacy and security. So, enforcers have started to address these non-price aspects of competition in their antitrust enforcement efforts.

In 2007, for example, the FTC investigated whether data privacy concerns justified blocking Google’s acquisition of DoubleClick. The FTC declined to block the acquisition on privacy grounds because ‘the sole purpose of federal antitrust review of mergers and acquisitions is to identify and remedy trans­actions that harm competition’.^[117] In dissent, however, Commissioner Pamela Jones Harbour wrote that the agency should take a ‘broader approach’ to its competitive analysis since the merger ‘combine[d] not only the two firms’ products and services, but also their vast troves of data about consumer behavior on the Internet’.^[118] Antitrust law, Commissioner Harbour said, has a role in regulating privacy abuses, and the FTC is ‘uniquely positioned’ to evaluate the competition and consumer protection implications of a merger.^[119]

Commissioner Harbour’s view has gained traction.^[120] In a statement in February 2024, for example, former FTC chair Lina Khan said that the FTC ‘recognize[s] the ways that consumer protection and competition enforcement are deeply connected with privacy violations fueling market power, and market power, in turn, enabling firms to violate consumer protection laws’.^[121]

This shift in official outlook extends to enforcement actions, in which enforcers now regularly cite data privacy issues as an element of consumer harm. In the Google Search case, for example, the complaint alleges that ‘[b]y restricting competition in general search services, Google’s conduct has harmed consumers by reducing the quality of search services (including dimensions such as privacy, data protection, and use of consumer data), lessening choice in search, and impeding innovation’.^[122] Further, in its monopolisation case against Facebook, the FTC accused the company of ‘engag[ing] in other activities that have degraded the user experience, including the misusing or mishandling of user data’.^[123]

As enforcers have sought to use diminished data privacy as a sword, companies have sought to use enhanced data privacy as a shield against accusations of anticompetitive conduct.^[124] The 2021 case between Epic Games and Apple is the leading example. Epic Games had alleged that Apple’s restrictions on alternative app stores and its requirement to use its own in-app payment system foreclosed competition in the market for global gaming transactions.^[125] Apple argued that these restrictions had pro-competitive justifications – they protected user privacy and data security. After a bench trial, the judge agreed.^[126]

A jury rejected a similar argument by Google in a parallel case brought by Epic Games. At trial, Google sought to show ‘procompetitive justifications for [its] conduct, including that its policies . . . protected users from malware’ and otherwise improved user security;^[127] but Epic Games put forward evidence that Google’s actions ‘deter app downloads and significantly degrade the consumer experience for downloading apps outside of Google Play, with limited to no improvement for deterring malware’.^[128] Google has said it will appeal the jury’s verdict.

Endnotes