Sub-Operating Systems: A New Approach to Application Security

In the current highly interconnected computing environments, users regularly use insecure software. Many popular applications, such as Netscape Navigator and Microsoft Word, are targeted by hostile applets or malicious documents, and might therefore compromise the integrity of the system. Current operating systems are unable to protect their users from these kinds of attacks, since the hostile software is running with the user\u27s privileges and permissions. We introduce the notion of the SubOS, a process-specific protection mechanism. Under SubOS, any application that might deal with incoming, possibly malicious objects, behaves like an operating system. It views those objects the same way an operating system views users - it assigns sub-user id\u27s - and restricts their accesses to the system resources

PATHALIAS or The Care and Feeding of Relative Addresses

Pathalias computes electronic mail routes in environments that mix explicit and implicit routing, as well as syntax styles. We describe the history of pathalias, its algorithms and data structures, and our design decisions and compromises. Pathalias is guided by a simple philosophy: get the mail through, reliably and efficiently. We discuss the principles of routing in heterogeneous environments necessary to make this philosophy a reality.http://deepblue.lib.umich.edu/bitstream/2027.42/107874/1/citi-tr-86-2.pd

Transient Addressing for Related Processes: Improved Firewalling by Using IPV6 and Multiple Addresses per Host

Traditionally, hosts have tended to assign relatively few network addresses to an interface for extended periods. Encouraged by the new abundance of addressing possibilities provided by IPv6, we propose a new method, called Transient Addressing for Related Processes (TARP), whereby hosts temporarily employ and subsequently discard IPv6 addresses in servicing a client host's network requests. The method provides certain security advantages and neatly finesses some well-known firewall problems caused by dynamic port negotiation used in a variety of application protocols. A prototype implementation exists as a small set of kame/BSD kernel enhancements and allows socket programmers and applications nearly transparent access to TARP addressing's advantages

When Enough is Enough: Location Tracking, Mosaic Theory, and Machine Learning

Since 1967, when it decided Katz v. United States, the Supreme Court has tied the right to be free of unwanted government scrutiny to the concept of reasonable xpectations of privacy.[1] An evaluation of reasonable expectations depends, among other factors, upon an assessment of the intrusiveness of government action. When making such assessment historically the Court has considered police conduct with clear temporal, geographic, or substantive limits. However, in an era where new technologies permit the storage and compilation of vast amounts of personal data, things are becoming more complicated. A school of thought known as “mosaic theory” has stepped into the void, ringing the alarm that our old tools for assessing the intrusiveness of government conduct potentially undervalue privacy rights. Mosaic theorists advocate a cumulative approach to the evaluation of data collection. Under the theory, searches are “analyzed as a collective sequence of steps rather than as individual steps.”[2] The approach is based on the recognition that comprehensive aggregation of even seemingly innocuous data reveals greater insight than consideration of each piece of information in isolation. Over time, discrete units of surveillance data can be processed to create a mosaic of habits, relationships, and much more. Consequently, a Fourth Amendment analysis that focuses only on the government’s collection of discrete units of trivial data fails to appreciate the true harm of long-term surveillance—the composite. In the context of location tracking, the Court has previously suggested that the Fourth Amendment may (at some theoretical threshold) be concerned with the accumulated information revealed by surveillance.[3] Similarly, in the Court’s recent decision in United States v. Jones, a majority of concurring justices indicated willingness to explore such an approach.[4] However, in general, the Court has rejected any notion that technological enhancement matters to the constitutional treatment of location tracking.[5] Rather, it has found that such surveillance in public spaces, which does not require physical trespass, is equivalent to a human tail and thus not regulated by the Fourth Amendment. In this way, the Court has avoided quantitative analysis of the amendment’s protections. The Court’s reticence is built on the enticingly direct assertion that objectivity under the mosaic theory is impossible. This is true in large part because there has been no rationale yet offered to objectively distinguish relatively short-term monitoring from its counterpart of greater duration. As Justice Scalia recently observed in Jones: “it remains unexplained why a 4-week investigation is ‘surely’ too long.”[6] This article suggests that by combining the lessons of machine learning with the mosaic theory and applying the pairing to the Fourth Amendment we can see the contours of a response. Machine learning makes clear that mosaics can be created. Moreover, there are also important lessons to be learned on when that is the case. Machine learning is the branch of computer science that studies systems that can draw inferences from collections of data, generally by means of mathematical algorithms. In a recent competition called “The Nokia Mobile Data Challenge,”[7] researchers evaluated machine learning’s applicability to GPS and cell phone tower data. From a user’s location history alone, the researchers were able to estimate the user’s gender, marital status, occupation and age.[8] Algorithms developed for the competition were also able to predict a user’s likely future location by observing past location history. The prediction of a user’s future location could be even further improved by using the location data of friends and social contacts.[9] Machine learning of the sort on display during the Nokia competition seeks to harness the data deluge of today’s information society by efficiently organizing data, finding statistical regularities and other patterns in it, and making predictions therefrom. Machine learning algorithms are able to deduce information—including information that has no obvious linkage to the input data—that may otherwise have remained private due to the natural limitations of manual and human-driven investigation. Analysts can “train” machine learning programs using one dataset to find similar characteristics in new datasets. When applied to the digital “bread crumbs” of data generated by people, machine learning algorithms can make targeted personal predictions. The greater the number of data points evaluated, the greater the accuracy of the algorithm’s results. In five parts, this article advances the conclusion that the duration of investigations is relevant to their substantive Fourth Amendment treatment because duration affects the accuracy of the predictions. Though it was previously difficult to explain why an investigation of four weeks was substantively different from an investigation of four hours, we now have a better understanding of the value of aggregated data when viewed through a machine learning lens. In some situations, predictions of startling accuracy can be generated with remarkably few data points. Furthermore, in other situations accuracy can increase dramatically above certain thresholds. For example, a 2012 study found the ability to deduce ethnicity moved sideways through five weeks of phone data monitoring, jumped sharply to a new plateau at that point, and then increased sharply again after twenty-eight weeks.[10] More remarkably, the accuracy of identification of a target’s significant other improved dramatically after five days’ worth of data inputs.[11] Experiments like these support the notion of a threshold, a point at which it makes sense to draw a Fourth Amendment line. In order to provide an objective basis for distinguishing between law enforcement activities of differing duration the results of machine learning algorithms can be combined with notions of privacy metrics, such as k-anonymity or l-diversity. While reasonable minds may dispute the most suitable minimum accuracy threshold, this article makes the case that the collection of data points allowing predictions that exceed selected thresholds should be deemed unreasonable searches in the absence of a warrant.[12] Moreover, any new rules should take into account not only the data being collected but also the foreseeable improvements in the machine learning technology that will ultimately be brought to bear on it; this includes using future algorithms on older data. In 2001, the Supreme Court asked “what limits there are upon the power of technology to shrink the realm of guaranteed privacy.”[13] In this piece, we explore an answer and investigate what lessons there are in the power of technology to protect the realm of guaranteed privacy. After all, as technology takes away, it also gives. The objective understanding of data compilation and analysis that is revealed by machine learning provides important Fourth Amendment insights. We should begin to consider these insights more closely. [1] Katz v. United States, 389 U.S. 347, 361 (1967) (Harlan, J., concurring). [2] Orin Kerr, The Mosaic Theory of the Fourth Amendment, 111 Mich. L. Rev. 311, 312 (2012). [3] United States v. Knotts, 460 U.S. 276, 284 (1983). [4] Justice Scalia writing for the majority left the question open. United States v. Jones, 132 S. Ct. 945, 954 (2012) (“It may be that achieving the same result [as in traditional surveillance] through electronic means, without an accompanying trespass, is an unconstitutional invasion of privacy, but the present case does not require us to answer that question.”). [5] Compare Knotts, 460 U.S. at 276 (rejecting the contention that an electronic beeper should be treated differently than a human tail) and Smith v. Maryland, 442 U.S. 735, 744 (1979) (approving the warrantless use of a pen register in part because the justices were “not inclined to hold that a different constitutional result is required because the telephone company has decided to automate”) with Kyllo v. United States, 533 U.S. 27, 33 (2001) (recognizing that advances in technology affect the degree of privacy secured by the Fourth Amendment). [6] United States v. Jones, 132 S.Ct. 945 (2012); see also Kerr, 111 Mich. L. Rev. at 329-330. [7] See Nokia Research Center, Mobile Data Challenge 2012 Workshop, http://research.nokia.com/page/12340. [8] Demographic Attributes Prediction on the Real-World Mobile Data, Sanja Brdar, Dubravko Culibrk & Vladimir Crnojevic, Nokia Mobile Data Challenge Workshop 2012. [9] Interdependence and Predictability of Human Mobility and Social Interactions, Manlio de Domenico, Antonio Lima & Mirco Musolesi, Nokia Mobile Data Challenge Workshop 2012. [10] See Yaniv Altshuler, Nadav Aharony, Michael Fire, Yuval Elovici, Alex Pentland, Incremental Learning with Accuracy Prediction of Social and Individual Properties from Mobile-Phone Data, WS3P, IEEE Social Computing (2012), Figure 10. [11] Id., Figure 9. [12] Admittedly, there are differing views on sources of authority beyond the Constitution that might justify location tracking. See, e.g., Stephanie K. Pell & Christopher Soghoian, Can You See Me Now? Toward Reasonable Standards for Law Enforcement Access to Location Data That Congress Could Enact, 27 Berkeley Tech. L.J. 117 (2012). [13] Kyllo, 533 U.S. at 34

Lawful Hacking: Using Existing Vulnerabilities for Wiretapping on the Internet

For years, legal wiretapping was straightforward: the officer doing the intercept connected a tape recorder or the like to a single pair of wires. By the 1990s, however, the changing structure of telecommunications—there was no longer just “Ma Bell” to talk to—and new technologies such as ISDN and cellular telephony made executing a wiretap more complicated for law enforcement. Simple technologies would no longer suffice. In response, Congress passed the Communications Assistance for Law Enforcement Act (CALEA) which mandated a standardized lawful intercept interface on all local phone switches. Since its passage, technology has continued to progress, and in the face of new forms of communication—Skype, voice chat during multiplayer online games, instant messaging, etc.—law enforcement is again experiencing problems. The FBI has called this “Going Dark”: their loss of access to suspects’ communication. According to news reports, law enforcement wants changes to the wiretap laws to require a CALEA-like interface in Internet software. CALEA, though, has its own issues: it is complex software specifically intended to create a security hole—eavesdropping capability—in the already-complex environment of a phone switch. It has unfortunately made wiretapping easier for everyone, not just law enforcement. Congress failed to heed experts’ warnings of the danger posed by this mandated vulnerability, and time has proven the experts right. The so-called “Athens Affair,” where someone used the built-in lawful intercept mechanism to listen to the cell phone calls of high Greek officials, including the Prime Minister, is but one example. In an earlier work, we showed why extending CALEA to the Internet would create very serious problems, including the security problems it has visited on the phone system. In this paper, we explore the viability and implications of an alternative method for addressing law enforcements need to access communications: legalized hacking of target devices through existing vulnerabilities in end-user software and platforms. The FBI already uses this approach on a small scale; we expect that its use will increase, especially as centralized wiretapping capabilities become less viable. Relying on vulnerabilities and hacking poses a large set of legal and policy questions, some practical and some normative. Among these are: (1) Will it create disincentives to patching? (2) Will there be a negative effect on innovation? (Lessons from the so-called “Crypto Wars” of the 1990s, and in particular the debate over export controls on cryptography, are instructive here.) (3) Will law enforcement’s participation in vulnerabilities purchasing skew the market? (4) Do local and even state law enforcement agencies have the technical sophistication to develop and use exploits? If not, how should this be handled? A larger FBI role? (5) Should law enforcement even be participating in a market where many of the sellers and other buyers are themselves criminals? (6) What happens if these tools are captured and repurposed by miscreants? (7) Should we sanction otherwise illegal network activity to aid law enforcement? (8) Is the probability of success from such an approach too low for it to be useful? As we will show, these issues are indeed challenging. We regard the issues raised by using vulnerabilities as, on balance, preferable to adding more complexity and insecurity to online systems

That Was Close! Reward Reporting of Cybersecurity “Near Misses”

Building, deploying, and maintaining systems with sufficient cybersecurity is challenging. Faster improvement would be valuable to society as a whole. Are we doing as much as we can to improve? We examine robust and long-standing systems for learning from near misses in aviation, and propose the creation of a Cyber Safety Reporting System (CSRS). To support this argument, we examine the liability concerns which inhibit learning, including both civil and regulatory liability. We look to the way in which cybersecurity engineering and science is done today, and propose that a small amount of ‘policy entrepreneurship’ could have substantial positive impact. We close by considering how a CSRS should be organized and housed

That Was Close! Reward Reporting of Cybersecurity “Near Misses”

Building, deploying, and maintaining systems with sufficient cybersecurity is challenging. Faster improvement would be valuable to society as a whole. Are we doing as much as we can to improve? We examine robust and long-standing systems for learning from near misses in aviation, and propose the creation of a Cyber Safety Reporting System (CSRS). To support this argument, we examine the liability concerns which inhibit learning, including both civil and regulatory liability. We look to the way in which cybersecurity engineering and science is done today, and propose that a small amount of ‘policy entrepreneurship’ could have substantial positive impact. We close by considering how a CSRS should be organized and housed

Design and Implementation of Virtual Private Services

Large scale distributed applications such as electronic commerce and online marketplaces combine network access with multiple storage and computational elements. The distributed responsibility for resource control creates new security and privacy issues, which are exacerbated by the complexity of the operating environment. In order to handle policies at multiple locations, the usual tools available (firewalls and compartmented file storage) get to be used in ways that are clumsy and prone to failure. We propose a new approach, virtual private services. Our approach relies on two functional divisions. First, we split policy specification and policy enforcement, providing local autonomy within the constraints of the global security policy. Second, we create virtual security domains, each with its own security policy. Every domain has an associated set of privileges and permissions restricting it to the resources it needs to use and the services it must perform. Virtual private services ensure security and privacy policies are adhered to through coordinated policy enforcement points. We describe our architecture and a prototype implementation, and present a preliminary performance evaluation confirming that our overhead of policy enforcement using is small

Risking Communications Security: Potential Hazards of the Protect America Act

A new US law allows warrantless wiretapping whenever one end of the communication is believed to be outside national borders. This creates serious security risks: danger of exploitation of the system by unauthorized users, danger of criminal misuse by trusted insiders, and danger of misuse by government agents