Source: http://illinoisjltp.com/timelytech/tag/search/
Timestamp: 2019-04-23 18:21:49+00:00

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In recent decades, network-driven data analysis has been a source of major developments and insights in neuroscience, sociology, and information science, just to name a few of the academic fields; these tools have also been used to develop precise product marketing initiatives, more appropriate recommendations on sites such as Pandora and Amazon, and efficient search algorithms such as Google’s PageRank. Curiously, legal research is typically not especially network-based, despite the fact that network tools such as PageRank were inspired by tools in legal analysis (especially Lexis’ Shepard Citations). It is a truism among legal scholars that statutes, enforcement, precedent and interpretation are all deeply interconnected. The significant role of stare decisis in contemporary legal practice makes it all the more puzzling why legal scholarship tends to be conducted in a linear or modular form. The aim of this article is to encourage a more network-theoretic approach to the identification and interpretation of legal precedent that more appropriately fits the non-modular, network structure of law.
I begin by briefly reviewing the basic concepts and tools of network analysis. Following this introduction, I highlight an important shortcoming in the most common tools for legal scholarship, and some concrete steps that could be taken to improve the methods used by lawyers and legal scholars to represent and interpret legal precedent. In particular, I argue that services such as WestlawNext and Lexis Advance could be improved if users were given more resources for going beyond simple Boolean searches. If properly implemented into the user interfaces of these services, network representations of legal precedent could make the process of searching and drawing from legal precedent more efficient, both in terms of the time taken to conduct searches and the accuracy of the results. I conclude by noting some directions for future research.
Networks have two components: objects and relations. The objects are called nodes and the relations between those objects are edges or vertices. In the network represented in Figure 1, the nodes are the numbered entities (1–10) and the edges are the lines connecting those entities. Not all edges are equal. If, for example, we represented a friendship network, it would be useful to distinguish between close friends and acquaintances. To track the strength of friendship ties, we could give distinct edge weights to each (e.g., two for close friends and one for acquaintances). In Figure 1, edge weight is represented by the color of the edge, with black edges representing strong ties and gray edges weak ties. If our representation of the network were sensitive to edge weight, 9 would be spatially closer to 8 than 10.
Figure 1. An example network with ten nodes and seventeen edges.
The creation of network representations usually involves attraction and repulsion between nodes. Edges between nodes act as attracting forces, with the edge weight determining the strength of the attraction. In order to preserve spatial distance between nodes, this attraction is countered by a general repulsive force between all nodes. To avoid unlimited repulsion between disconnected nodes, a gravitational force pulls all nodes to the center.
The most significant properties of nodes, for present purposes, are their relational properties. Degree, a basic relational property, is equal to the number of the node’s edges. In Figure 1, node 2 has a degree of four because it is related to four other nodes. Degree is a limited measure because it only considers nodes in relation to their nearest neighbors and is insensitive to the significance of the connection. In a trade network, for example, it would be important to know not just which countries trade with which, but also the quantity of goods traded. To track this information, we should consider weighted degree, which assigns distinct values to each edge based on the significance of that relation, but this information is still highly limited. In analyzing a criminal or terrorist network, for example, we can learn something from the fact that A communicated with B, but we learn far more about A if we also know that B worked with C, D, and E, where these are high level figures in the illicit organization.
To track such indirect connections, we also need a measure of network centrality. Various centrality algorithms are used for different purposes, but they share an important common feature: sensitivity to a node’s position in the network as a whole. Here I mention just three. The first, betweenness centrality, is a measure of how often a node occurs in the shortest path between two other nodes. Nodes with higher betweenness centrality are more likely to play an essential bridge role in connecting two otherwise separate groups of nodes. In Figure 1, node 8 has the highest betweenness centrality because 9 and 10 are only related to other nodes through 8. In a network of U.S. senators, with edges defined by voting records, centrist senators would have the highest betweenness centrality because they alone bridge the divide between Republican and Democrat voting blocks. Eigenvector centrality is a measure of the importance of a node in the network as measured by its connectedness to other nodes with high Eigenvector centrality. This metric is similar to the third measure of centrality, Google’s PageRank metric for determining the relevance of websites in a search, which in turn is inspired by Shepardizing. The PageRank for website W is determined by considering the number of other websites with links to W, with greater weight given to linking websites that are themselves frequently linked. Above, node 7 has the highest Eigenvector centrality and PageRank because it has several connections with nodes that themselves have several connections. In a citations-based network, Eigenvector centrality is a measure for the relative centrality of an author to the discussion in their area of specialty.
For present purposes, we can think of individual court opinions as nodes in the network. The most significant edges in the network are citations to previous court opinions, but one could also conceptualize the legal precedent framework with edges indicating similarity of content, geographical regions, or time periods. Whatever data are chosen as the basic structure of the network, legal scholars could, as I argue below, benefit from a network-theoretic reconceptualization of the legal terrain.
Online research tools such as WestlawNext and Lexis Advance already have limited network-based approaches, but these services could be substantially improved by extending the user’s ability to visualize and digest the interconnected network of cases constituting current legal precedent. In this section I present several ways that these services could be enhanced. Each of the suggested changes would be relatively easy to implement and could significantly improve scholars’ and lawyers’ ability to identify the most relevant precedents. These suggestions apply to Westlaw, LexisNexis, and other similar services, but I will focus on the current user interface of WestlawNext and leave it to the reader to see how the suggestions would apply to other services.
For WestlawNext, generalized inquiry usually begins with the user providing a citation, party names, keywords, or other information into a Boolean search algorithm. While this process is fairly straightforward and efficient, it has a notable shortcoming. If, for example, your aim is to find cases involving pre-verbal infants causing harm, a search for “baby” will return just those cases where “baby” appears as a keyword or within the text; but, of course, cases mentioning “infant,” “toddler,” “small child,” or “newborn” could also prove relevant. Thus, these search engines could be improved by implementing semantic network databases such that nearby terms are given some weight.
Once the user has found a relevant case, WestlawNext provides excellent network-based information in the form of KeyCite. This tool allows users to immediately see a summary evaluation of how the case fits into the network of legal precedent, whether the case has been superseded, affirmed, distinguished, or received other treatments, and the significance of each related case. This information is analogous to knowing node degree, types of edges, nearest neighbors, and edge weight, but is limited in the same way as these measures of node significance. A major shortcoming of the initial search results is that users are given a list of cases, C1–Cn, each related to the queried case, C0, where C1–Cn are each provided with specific information linking it to C0, but without any further information putting these cases in a broader legal context or showing how they might directly relate to one another. This is partially remedied by the diagrammatic representation of the case history on WestlawNext, wherein users see a small set of prior cases that have been granted rehearing, had their judgment reversed, etc., but there is a great missed opportunity at this stage of the search. Along with learning how the case directly relates to prior cases, it would be valuable to have network-based representations of a greater diversity of relations and a ranking system more sensitive to a case’s position within the network of legal precedents. Researchers could benefit from visual representations of several clusters of cases relevant to their specific topics, where the edges would indicate important relations between these cases beyond the relation of explicit undermining or supporting relations. For example, one could selectively add or remove edges indicating similarity in semantic content, relevant statutes, or topics. This would be beneficial for allowing scholars to freely navigate the metaphorical legal space in a literal physical space that intuitively maps onto the conceptual distances between the various cases. When starting the research process, this would provide users with an easily digestible, unified picture of the topic highlighting the most important judgments to consider in more detail, and, for the users already familiar with the legal landscape, this service would help them identify the most important gaps in their knowledge. For most of the relevant criteria, both Westlaw and LexisNexis already possess the data, so these services could be improved simply by adding functionality to the user interface.
It would also be beneficial to use network-based measures of centrality as an indicator of the significance of cases rather than raw citation counts or merely relying on a vague sense of importance that one has inherited from peers and educators. If one wished to know the most significant landmark cases on a specific issue, one could do far worse than seeking experts’ opinions, but a quantitative measure of citation counts may be a more reliable indicator of significance than even the intuitive judgments of experts. WestlawNext provides these citation numbers, but raw citation counts can be highly misleading as a method for ranking the significance of cases because this data is not sensitive to the relative importance of the court decisions citing the case in question, and some cases have received more citations simply in virtue of the fact that they were decided earlier. By analogy, in an academic citation network, being cited by the top scholar in the field is more important than being cited by ten small players. In the same way, court decisions cited in landmark cases are more significant than those cited by several less significant cases.
To gain a more accurate representation of the most significant cases, it would be better to have a system that mirrors academic rankings like H-index or Google’s algorithms for ranking websites. This could be implemented by WestlawNext and similar services by providing users with a significance score for case C that is simultaneously sensitive to all of these factors: (1) the number of cases citing and cited by C, (2) the significance of the cited cases to C and the significance of C in the court decisions citing it, and (3) the relative importance of the citing and cited cases. This sort of method has been tested by James Fowler et al., who found inward relevance (one of many measures of network centrality) was a strong predictor of future citations. Given the relative success of this and similar models for accurately identifying and predicting case significance, online archives such as Westlaw could improve the relevance of their search results by using network centrality for sorting and filtering results, and they could provide more meaningful information to users by including cases’ centrality scores in the listed search results.
The advice offered above is specifically aimed at improving the efficiency of searches for cases with legal precedent, but these tools could be used in a greater variety of contexts. I conclude by briefly suggesting a few further possibilities. Closely related to the discussion above, the method of collecting and analyzing case precedent from a network perspective could be used by legal scholars to develop highly accurate pictures of the history and future of law. For example, Fowler et al. observed that, in the cases they reviewed, the Commerce Clause was the most significant legal issue in 1955, whereas First Amendment issues had become dominant in more recent years.
By identifying and tracking the trends in law over the years, researchers could develop fine-grained, data-driven overviews of the history of the law while also developing accurate models for predicting future trends. Second, scholars could use network analysis to test for possible sources of bias in judicial decisions over the years by creating and analyzing social networks showing social or communication links between judges and lawyers that correlate, in a problematic way, with judges’ rulings. Finally, similar methods could be used to compare the structures of scientific and legal citation networks to see if the legal community’s structure is relevantly similar to the structure of the sciences.
*Ph.D., Philosophy, University of Illinois. Special thanks go to Laura Peet and Alexis Dyschkant for invaluable discussions regarding the nature and practice of law. I also wish to thank Jonathan Waskan and Jana Diesner for providing the empirical and theoretical tools needed to approach this topic.
 Simon Haykin, Neural Networks: A Comprehensive Foundation (1st ed. 1994).
 The SAGE Handbook of Social Network Analysis (John Scott & Peter Carrington eds., 2011).
 Ravinda Ahuja, Thomas Magnanti, & James Orlin, Network Flows: Theory, Algorithms, and Application (1993).
 Pandora, http://www.pandora.com (last visited Feb. 4, 2015).
 Amazon, http://www.amazon.com (last visited Feb. 4, 2015).
 Ian Rogers, The Google Pagerank Algorithm and How it Works, Ian Rogers, http://www.sirgroane.net/google-page-rank/ (last visited Feb. 4, 2015).
 Eugene Garfield, Discovering Shepard’s Citations, WebOfStories, http://www.webofstories.com/ play/eugene.garfield/25;jsessionid=C829679D889485A4E6AF76C0C3286EF1 (last visited Feb. 4, 2015).
 Ronald Dworkin, Law’s Empire (1986).
 David Easley & Jon Kleinberg, Networks, Crowds, and Markets: Reasoning About a Highly Connected World 2 (2010).
 Reinhard Diestel, Graph Theory 5 (3d ed. 2005).
 Easley & Kleinberg, supra note 10, at 434.
 See id. at 417. This may seem paradoxical, as Eigenvector centrality for any given node can only be determined in reference to the Eigenvector centrality of other nodes. The paradox is removed because this metric is calculated on the basis of several iterations of the algorithm.
 The Page Rank Algorithm, eFactory, http://pr.efactory.de/e-pagerank-algorithm.shtml (last visited Feb. 4, 2015).
 WestlawNext, http://www.westlawnext.com (last visited Feb. 4, 2015).
 LexisNexis, http://www.lexisnexis.com (last visited Feb. 4, 2015).
 Lexis Advance offers a similar service with Shepard’s, and its Map option mirrors WestlawNext’s case mapping function described later in the paragraph.
 Publish or Perish,Harzing, http://www.harzing.com/pop.htm (last visited Feb. 4, 2015). H-index is a measure of academics’ productivity. A scholar is given a score of h where she has h papers with h publications and the remaining papers have less than or equal to h citations.
 James Fowler et al., Network Analysis and the Law: Measuring the Legal Importance of Precedents at the U.S. Supreme Court, 15 Pol. Analysis 324–46 (2007).
In Riley v. California, the Court held that a warrant is required for all searches of cellular phones regardless of whether the search is incident to a lawful arrest. The Court reasoned that the traditional considerations for the “search-incident-to-arrest doctrine” are not applicable to the capacity and nature of data stored in modern smart-phones. Riley is a resounding victory for “privacy specialists” and advocates of a digital approach to the Fourth Amendment doctrine. Yet determining whether Riley carries those aspirations to fruition requires the Court to keep up with the rapid changes in the smart-phone industry. It is almost certain that a smart-phone model would become either obsolete within a year or unfashionable within a shorter time period. The technological advances that are made with every “jump” to the next generation are, to the untrained-eye, somewhat nonexistent. The legal possibilities, however, are ever changing, as the functions of a handheld device resemble less those of a landline than those of a personal computer. Hence, the next challenge for the Court may be the use of biometric technology (or Biometrics) in smart-phones and computing tablets.
Biometrics “take[s] something unique to the individual—a fingerprint, an iris, voice or facial features—as authentication” to identify that person. The latest iterations of the Apple iPhone use Biometrics—the Touch ID—to allow customers to access to their device by using their fingerprints. Each iPhone is equipped with “Touch ID . . . capable of 360-degree readability” allowing rapid access to the device without the added step of entering the previously required four-digit personal identification number (PIN). Hours after its initial announcement privacy concerns were voiced. This essay, however, addresses a separate question: whether a search warrant requires a person to bypass the biometric lock on his phone.
Protection could be through the Fifth Amendment’s Privilege against Self-Incrimination. The privilege is violated when a person is forced to communicate information that would lead to evidence incriminating him in the commission of a crime. There are three requirements in this prohibition. First, there must be compulsion or involuntary disclosure. Second, the information obtained must be incriminating against the person providing it. However, the person need only have a belief the information is incriminating. And finally, the compulsion must apply to conduct that is communicative. Thus, the Court’s Self-Incrimination cases look at whether there was a testimonial communication. Hence three factual patterns raise the Self-Incrimination clause: (1) physical exhibition; (2) production of evidence; and (3) information attesting to the defendant’s state of mind. The Court drew distinctions between “real or physical evidence” and evidence of factual assertions. Because of its nature, any information gleaned from fingerprints through the Touch ID would be considered as “real or physical evidence” and be barred from Self-Incrimination protection under the first line of cases.
Stemming from Holt v. United States, these cases hold that the exhibition of certain “physical characteristics” is not dispositive of the accused’s belief of his guilt. In Holt, the Court reviewed the guilty verdict of a murder trial. Holt, the defendant, argued that the trial court could not compel him to wear a blouse allegedly belonging to the murderer. The Court found that this claim was “an extravagant extension of the [Fifth] Amendment.” The Court reasoned that trial courts are required to compel defendants to come forward in criminal cases to allow the “jury to look at [him] and compare his features with a photograph in proof.” The Court reasoned that, no “statements” were made and the order did not “extort [any] communications . . . from him . . . .” The Court understood the Fifth Amendment as not abrogating the state power to compel the physical exhibition of defendants in a criminal matter. Since Holt, the Court has held that compulsion to participate in police lineups, to provide voice exemplars, to submit to a blood test or a roadside sobriety test, or to give a handwriting sample are not protected by the Self-Incrimination clause.
We do not disagree with the dissent that the expression of the contents of an individual’s mind is testimonial communication for purposes of the Fifth Amendment. We simply disagree with the dissent’s conclusion that the execution of the consent directive at issue here forced petitioner to express the contents of his mind. In our view, such compulsion is more like being forced to surrender a key to a strongbox containing incriminating documents than it is like being compelled to reveal the combination to [petitioner’s] wall safe.
The majority accepted the distinction, but it pointed that the disagreement between Justice Stevens and his colleagues was whether the directive was a key or a combination.
Stevens’ position did not win the day, and his distinction may have gone unnoticed if not for United States v. Hubbell. In Hubbell the Court held that producing documents pursuant to a subpoena, which does not describe with particularity the documents sought, violates the Self-Incrimination clause. Justice Stevens, now writing for an eight-member majority, stated, “[the] assembly of those documents was like telling an inquisitor the combination to a wall safe, not like being forced to surrender the key to a strongbox.” The distinction was reiterated and entered the Court’s jurisprudence if only as a dictum. Nonetheless, it is relevant to our concerns.
Two things are clear from Doe II’s footnote 9. First, a defendant cannot be compelled to reveal the content of his mind. This proposition is inherent from the Self-Incrimination clause and the Court’s line of cases on physical characteristics. A defendant cannot reveal his thoughts and knowledge without bearing testimony to his guilt—that is a given.
Second, producing the key to a strongbox, unlike providing the combination to a wall safe, is not testimonial of the defendant’s content of his mind. Put differently, the possession of a key—even if it is the only copy—does not testify that the possessor is aware of the content of the strongbox. When stated as such, this proposition is obvious and has no practical application. But it may yet be incorrect. For one, the production of tangible evidence is indicative, almost akin to testimony, of “existence, control, and location of” potentially incriminating evidence. Equally so, in providing the key upon request, the arrestee is clearly stating that he is aware of its purpose. Yet even if this notion stands, this would suggest that if a person is privy to the combination of a safe he is automatically privy to its contents. That is simply incongruent. One can point to several persons sharing the combination of their safe with guarantors—if only to insure against memory failure. But if the Court were suggesting that knowledge of the combination creates a temptation to investigate its content and thus leading to testimonial knowledge, what would prevent the detainer of the key from pursuing the same end? The truth is simple: the Court accepted the distinction without elaboration.
If fingerprint locks are comparable to key locks, then the privilege does not insulate the owner of the iPhone from being compelled to provide access to it. But if they are more like the combination locks, then the privilege applies. At first glimpse, a thumbprint is more like a key. It is tangible. It is mechanical in its application. And it requires no recollection of memory in order to operate it. This lack of memorization may have created this odd distinction. But the differences are more compelling. A fingerprint does more than provide access. It could be use to authenticate identity. Hence the fingerprint—much like the PIN—must be chosen as the exact means of access to the device; and thus, although to a minimum, testify that there was a conscious thought process applied in its selection.
The strongbox analogy does nothing to resolve this issue. It is clear that the Court would not entertain compelled acquisition of a safe combination, PIN or Password. However the analogy creates more uncertainty than it would resolve. Fingerprint locks are more like passwords and usernames when used in conjunction to gain access. It identifies the user and authenticates his identity all in one motion.
However, if biometric access suggests identity and control, the problem is perhaps moot, since all searches must be pursuant to a warrant, and hence subject to the particularity requirement. This is not necessarily true. Evidence seized from smart-phones may not be accessible unless police officers can bypass the biometric locks. Yet, regardless of a warrant, a defendant is under no obligation “to aid” police officers in accessing evidence on his device. In Andresen v. Maryland, the Court addressed whether the Self-Incrimination clause precludes the prosecution from introducing evidence seized pursuant to a search warrant in its case-in-chief against the defendant. The evidence at issue was “business records [containing] statements made by the petitioner.” The Court held that “suppression” was not compelled because the defendant “was not asked to say or to do anything.” The Court said, “when these records were introduced at trial, they were authenticated by a handwriting expert, not by” the defendant.
However in reaching this conclusion the Court was careful to separate the Fifth Amendment problem from the defendant’s obligations vis-à-vis the police investigation under a search warrant: “a seizure of [incriminating] materials by law enforcement officers differs [from production of the same materials in compliance with a subpoena], in a crucial respect . . . . [The] individual against whom the search is directed is not required to aid in the discovery, production, or authentication of incriminating evidence.” Andresen conflicts with the strongbox analogy found in Doe II and Hubbell. But it is clear from Andresen that a defendant need not “aid” the police in its investigation. Providing the key to a strongbox, one clearly insurmountable by any other means, is providing aid in a criminal investigation.
If Andresen is still good law, it stands that a warrant cannot compel the defendant to “aid” in his indictment, arrest or prosecution. The Fifth Amendment guarantees this protection, and the Fourth Amendment does not deplete it. The state cannot circumvent one constitutional prohibition by satisfying another. Meeting all of the requirements for a valid warrant cannot—and should not—allow police officers to compel criminal suspects to assist in their own prosecution. Thus police officers seeking to access a smart-phone must bypass the biometric lock by means independent from compelling the defendant to do so.
*J.D. University of Illinois (’14); M.Sc. Applied Economics, Florida State University (’09). Special thanks go to Benjamin Sunshine for bringing this topic to my attention. I also give thanks my Professors: Margareth Etienne, Andrew Leipold, and Shannon Moritz, as well as Dean Jamelle Sharpe for their advice. Thanks are also due to my friends: Michael Corliss, and Derek Dion, in conjunction with the Editing Board at the Illinois Journal of Law, Tech. & Policy for their support and indulgence. Finally I thank my family and loved ones, in particular my parents.
 Id. at 2485 (“[O]fficers must generally secure a warrant before conducting such a search.”).
 Id. (declining to extend United States v. Robinson, 414 U.S. 218 (1973)).
 Richard Re, Symposium: Inaugurating the Digital Fourth Amendment, SCOTUSblog (June 26, 2014, 12:37 PM), http://www.scotusblog.com/2014/06/symposium-inaugurating-the-digital-fourth-amendment/.
 See Suzanne Choney, Planned Obsolescence: Cell Phone Models, NBC News (Feb. 24, 2009, 8:57 AM), (http://www.nbcnews.com/id/29258026/ns/technology_and_science-digital_home/t/planned-obsolescence-cell-phone-models/ (“[M]ost phones have a market life cycle of nine to 12 months.”).
 See id. (stating a new model may “still look like the original . . . but . . . has a few new features”).
 Riley v. California, 134 S. Ct. 2473, 2489 (2014) (“The term ‘cell phone’ is itself misleading shorthand; many of these devices are in fact minicomputers that also happen to have the capacity to be used as a telephone.”) (emphasis added).
 Mark G. Milone, Biometric Surveillance: Searching For Identity, 57 Bus. Law 497, 497 (2001) (“Biometrics use immutable personal characteristics, such as facial features, fingerprints, and retinal patterns, to establish and authenticate identity.”).
 Touch ID. Security. Right at Your Fingertip., Apple, https://www.apple.com/iphone-6/touch-id/ (last visited Oct. 4, 2014).
 Id.; see also David Pogue, In Arrival of 2 iPhones, 3 Lessons, N.Y. Times (Sept. 17, 2013), http://www.nytimes.com/2013/09/18/technology/personaltech/in-arrival-of-2-iphones-3-lessons.html (“[Y]es, a password is a hassle; half of [smart-phone] users never bother setting one up.”).
 Apple Fingerprint Tech Raises “Privacy Questions,” BBC News (Sept. 20, 2013, 1:28 PM), http://www.bbc.co.uk/news/technology-24177851 (“Senator Al Franken, chairman of the influential Senate Judiciary Subcommittee on Privacy, Technology and the Law, has written to Apple boss Tim Cook explaining his security concerns.”).
 See U.S. Const. amend. V (“No person shall . . . be compelled in any criminal case to be a witness against himself.”); see also Holt v. United States, 218 U.S. 245, 252–53 (1910) (expressing the self-incrimination as a “prohibition of the use of [extorted] communications”).
 Holt, 218 U.S. at 252–53 (stating the clause “is a prohibition [against] the use of physical or moral compulsion”).
 That is simply found in the language of the Amendment. Cf. U.S. Const. amend. V (“No person shall . . . be compelled in any criminal case to be a witness against himself.”).
 Cf. Pennsylvania v. Muniz, 496 U.S. 582, 615 (1990) (Rehnquist, C.J., concurring) (“By ‘incriminating response’ we refer to any response—whether inculpatory or exculpatory—that the prosecution may seek to introduce at trial.”) (internal citation omitted).
 Holt, 218 U.S. at 252–53 (stating the clause “is a prohibition [against extorting] communication”).
 Id. at 245; Schmerber v. California, 384 U.S. 757 (1967); United States v. Wade, 388 U.S. 218 (1967); Gilbert v. California, 388 U.S. 263 (1967); United States v. Dionisio, 410 U.S. 1 (1973); Pennsylvania v. Muniz, 496 U.S. 582 (1990).
 Fisher v. United States, 425 U.S. 391 (1976); United States v. Doe (Doe I), 465 U.S. 605 (1984); Doe v. United States (Doe II), 487 U.S. 201 (1988).
 Muniz, 496 U.S. at 591.
 Holt, 218 U.S. at 245.
 Schmerber v. California, 384 U.S. 757, 761 (1966).
 United States v. Wade, 388 U.S. 218, 222 (1967).
 Id. at 222–23; United States v. Dionisio, 410 U.S. 1, 17–18 (1973).
 Schmerber, 384 U.S. at 761.
 Pennsylvania v. Muniz, 496 U.S. 582, 590 (1990). The Court however framed its ruling to only include test that would not require the accused from giving an answers the veracity of which can become testimonial. Id. at 600.
 Gilbert v. California, 388 U.S. 263, 266–67 (1967).
 Doe v. United States, 487 U.S. 201 (1988).
 Id. at 219–21 (Stevens, J., Dissenting).
 Id. at 210 n.9 (internal citation and quotation marks omitted) (emphasis added).
 United States v. Hubbell, 530 U.S. 27 (2000).
 Id. at 43 (citing Doe II, 487 U.S. at 210 n.9).
 This part is almost self-evident. Safe combinations, much like passwords, require mental recollection.
 Practically speaking, combination locks are nothing more than the evolutionary successors of key locks.
 Adam M. Gershowitz, Password Protected? Can a Password Save Your Cell Phone from a Search Incident to Arrest?, 96 Iowa L. Rev. 1125, 1171 (2011) (citing Fisher v. United States, 425 U.S. 391, 410 (1976); Commonwealth v. Hughes, 404 N.E.2d 1239, 1244–45 (Mass. 1980)).
 One answer is that a person may be in possession of a key without knowing its purpose. That cannot be the answer because a person can also know a sequence of numbers without knowing its meaning. For example: what is the meaning of 01.02.54?
 The Strongbox analogy gets more complicated when we expand its reach. Take the following two scenarios that paradoxically lead to opposing results. First if a person has four keys on a keychain, one of which opens a door, he can be compelled to identify which one opens the door. Yet consider a door that has four locks, and the same key opens all four. If, to open the door, the person must always unlock the locks in a given sequence—almost like a safe combination—then it stems from Dow II that he cannot be compelled to tell the authorities which sequence, even though he is required to provide the key.
 See Gershowitz, supra note 48 at 1171 (suggesting PINs are testimonial because they “reveal the contents of [a person’s] mind by recalling” the sequence).
 Kristian Köhntopp, Comment to Fingerprints are Usernames, not Passwords, Dustin Kirland (Oct. 7, 2013), http://blog.dustinkirkland.com/2013/10/fingerprints-are-user-names-not.html (“We could each conveniently identify ourselves by our fingerprint.”).
 See Apple, supra note 10 (explaining the process for calibrating the fingerprint authentication system).
 See, e.g., In re Boucher, No. 2:06-mj-91, 2007 WL 4246473, at *2 (D. Vt. Nov. 29, 2007), rev’d No. 2:06-mj-91, 2009 WL 424718 (D. Vt. Feb. 19, 2009) (denying prosecutors request that the subject of Grand Jury subpoena provides access to his computer by entering the password, even if done privately in a secluded room).
 But see Köhntopp, supra note 52 (suggesting “biometrics cannot, and absolutely must not, be used to authenticate an identity”).
 Cf. Riley v. California, 134 S. Ct. 2473, 2485 (2014) (“[O]fficers must generally secure a warrant before conducting such a search.”). The particularity requirement would overcome the generality of the subpoena found in Hubbell.
 Andresen v. Maryland, 427 U.S. 463, 473–74 (1976).
 Id. at 474. This answer reinforced a consistent theme requiring an intermediary separating an inference of guilt from the conduct. See, e.g., Holt v. United States, 218 U.S. 245 (1910) (requiring jury to authenticate physical match); Schmerber v. California, 384 U.S. 757 (1967) (requiring blood testing); United States v. Wade, 388 U.S. 218 (1967) (requiring voice matching); Gilbert v. California, 388 U.S. 263 (1967) (requiring hand exemplar matching).
 Id. at 473–74 (emphasis added).
 Cf. id. This is true both from Andresen, and the language and spirit of the Bill of Rights.

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