arXiv subjects

Joan Feigenbaum

Publications and source records attributed to Joan Feigenbaum.

17 recordsLinked to original sources

arXiv2026

Report on the Designing Accountable Software Systems Workshop

The Workshop on Designing Accountable Software Systems (DASS) was convened in November 2024 with support from the U.S. National Science Foundation to engage a wide range of current and future stakeholders from government, academia, and industry on the cross-disciplinary topic of accountability in software systems. Over two days, attendees engaged in a series of panels, invited talks, and breakout sessions covering: (1) the dimensions of accountability, including legal compliance as well as business and societal aspects and drivers; (2) a conceptual model of the various structures needed to realize accountability; (3) the sources of legal requirements that affect software; (4) the operationalization of legal requirements in software; (5) the requirements to preserve evidence needed to conduct investigations; and (6) a range of challenges and contextual factors beyond software that affect why some accountability structures succeed, while others fail. The workshop was conducted as a collaborative systematization of knowledge that culminated in several research directions. The findings include the importance of clarifying definitions and responsibilities within accountable organizations, which can affect whether those researching accountability are making assumptions that limit the generalizability of findings. Further research was also identified as needed to study the ways to improve the translation of accountability structures into the software design process while improving engagement with stakeholders, such as legislators, regulators, business executives and system developers. Finally, a key finding was the high demands that DASS-like research projects place on interdisciplinary teams: both in terms of team formation and sustainment, as well as, the specific demands of cross-disciplinary learning that covers both research methods, research dissemination, and career development.

cs.SE↗

arXiv2025

SoK: Scope and Mission of CS&Law

We systematize the intellectual scope of the ACM Computer Science and Law Symposium (CS&Law). In particular, we address the meaning and importance of the word ''and'' in the name of the symposium. We identify previously published papers (from CS&Law and other forums) that exemplify different aspects of the CS&Law scope and note that the scope is expected to evolve as the symposium and the community grow and change. To round out our systematization of the still nascent research area, we also discuss the mission of CS&Law: What might the symposium seek to accomplish beyond providing a forum for intellectual exchange and community formation?

cs.DL↗

arXiv2025

PCSI -- The Platform for Content-Structure Inference

The Platform for Content-Structure Inference (PCSI, pronounced "pixie") facilitates the sharing of information about the process of converting Web resources into structured content objects that conform to a predefined format. PCSI records encode methods for deriving structured content from classes of URLs, and report the results of applying particular methods to particular URLs. The methods are scripts written in Hex, a variant of Awk with facilities for traversing the HTML DOM.

cs.IR↗

arXiv2022

From Data Leverage to Data Co-Ops: An Institutional Model for User Control over Information Access

Internet companies derive value from users by recording and influencing their behavior. Users can pressure companies to refrain from certain invasive and manipulative practices by selectively withdrawing their attention, an exercise of data leverage as formulated by Vincent et al. Ligett and Nissim's proposal for an institution representing the interests of users, the data co-op, offers a means of coordinating this action. We present one possible instantiation of the data co-op, including the Platform for Untrusted Resource Evaluation (PURE), a system for assigning labels provided by untrusted and semi-trusted parties to Internet resources. We also describe PURESearch, a client program that re-ranks search results according to labels provided by data co-ops and other sources.

cs.CY↗

arXiv2022

On Heuristic Models, Assumptions, and Parameters

Insightful interdisciplinary collaboration is essential to the principled governance of technology. When such efforts address the interaction between computation and society, they often focus on modeling, the process by which computer scientists formally define problems in order to enable algorithmic solutions. But modeling is a multifaceted and inherently imperfect process. Especially in interdisciplinary work, it often receives uneven scrutiny because of the practical challenges of communicating complex technical details to non-experts. We argue that there is an underappreciated if loose family of obscure and opaque technical caveats, choices, and qualifiers that the social effects of computing can depend just as much on as far more heavily scrutinized modeling choices. These artifacts are often used by researchers to paper over the incomplete theoretical foundations of computing or to burden shift responsibility for the impact of normative design decisions. Further, their nuanced technical nature often complicates thorough sociotechnical scrutiny of the discretionary decisions made to manage them. We describe three specific classes of such objects: heuristic models, assumptions, and parameters. We raise six reasons these objects may be hazardous to comprehensive analysis of computing and argue they deserve deliberate consideration as researchers explain scientific work.

cs.CY↗

arXiv2021

PriFi: Low-Latency Anonymity for Organizational Networks

Organizational networks are vulnerable to traffic-analysis attacks that enable adversaries to infer sensitive information from the network traffic - even if encryption is used. Typical anonymous communication networks are tailored to the Internet and are poorly suited for organizational networks. We present PriFi, an anonymous communication protocol for LANs, which protects users against eavesdroppers and provides high-performance traffic-analysis resistance. PriFi builds on Dining Cryptographers networks but reduces the high communication latency of prior work via a new client/relay/server architecture, in which a client's packets remain on their usual network path without additional hops, and in which a set of remote servers assist the anonymization process without adding latency. PriFi also solves the challenge of equivocation attacks, which are not addressed by related works, by encrypting the traffic based on the communication history. Our evaluation shows that PriFi introduces a small latency overhead (~100ms for 100 clients) and is compatible with delay-sensitive applications such as VoIP.

cs.CR↗

arXiv2020

Privacy-Preserving XGBoost Inference

Although machine learning (ML) is widely used for predictive tasks, there are important scenarios in which ML cannot be used or at least cannot achieve its full potential. A major barrier to adoption is the sensitive nature of predictive queries. Individual users may lack sufficiently rich datasets to train accurate models locally but also be unwilling to send sensitive queries to commercial services that vend such models. One central goal of privacy-preserving machine learning (PPML) is to enable users to submit encrypted queries to a remote ML service, receive encrypted results, and decrypt them locally. We aim at developing practical solutions for real-world privacy-preserving ML inference problems. In this paper, we propose a privacy-preserving XGBoost prediction algorithm, which we have implemented and evaluated empirically on AWS SageMaker. Experimental results indicate that our algorithm is efficient enough to be used in real ML production environments.

cs.CR↗

arXiv2017

Avoiding The Man on the Wire: Improving Tor's Security with Trust-Aware Path Selection

Tor users are vulnerable to deanonymization by an adversary that can observe some Tor relays or some parts of the network. We demonstrate that previous network-aware path-selection algorithms that propose to solve this problem are vulnerable to attacks across multiple Tor connections. We suggest that users use trust to choose the paths through Tor that are less likely to be observed, where trust is flexibly modeled as a probability distribution on the location of the user's adversaries, and we present the Trust-Aware Path Selection algorithm for Tor that helps users avoid traffic-analysis attacks while still choosing paths that could have been selected by many other users. We evaluate this algorithm in two settings using a high-level map of Internet routing: (i) users try to avoid a single global adversary that has an independent chance to control each Autonomous System organization, Internet Exchange Point organization, and Tor relay family, and (ii) users try to avoid deanonymization by any single country. We also examine the performance of Trust-Aware Path selection using the Shadow network simulator.

cs.CR↗

arXiv2016

Open, privacy-preserving protocols for lawful surveillance

The question of how government agencies can acquire actionable, useful information about legitimate but unknown targets without intruding upon the electronic activity of innocent parties is extremely important. We address this question by providing experimental evidence that actionable, useful information can indeed be obtained in a manner that preserves the privacy of innocent parties and that holds government agencies accountable. In particular, we present practical, privacy-preserving protocols for two operations that law-enforcement and intelligence agencies have used effectively: set intersection and contact chaining. Experiments with our protocols suggest that privacy-preserving contact chaining can perform a 3-hop privacy-preserving graph traversal producing 27,000 ciphertexts in under two minutes. These ciphertexts are usable in turn via privacy-preserving set intersection to pinpoint potential unknown targets within a body of 150,000 total ciphertexts within 10 minutes, without exposing personal information about non-targets.

cs.CR↗

arXiv2015

Reuse It Or Lose It: More Efficient Secure Computation Through Reuse of Encrypted Values

Two-party secure function evaluation (SFE) has become significantly more feasible, even on resource-constrained devices, because of advances in server-aided computation systems. However, there are still bottlenecks, particularly in the input validation stage of a computation. Moreover, SFE research has not yet devoted sufficient attention to the important problem of retaining state after a computation has been performed so that expensive processing does not have to be repeated if a similar computation is done again. This paper presents PartialGC, an SFE system that allows the reuse of encrypted values generated during a garbled-circuit computation. We show that using PartialGC can reduce computation time by as much as 96% and bandwidth by as much as 98% in comparison with previous outsourcing schemes for secure computation. We demonstrate the feasibility of our approach with two sets of experiments, one in which the garbled circuit is evaluated on a mobile device and one in which it is evaluated on a server. We also use PartialGC to build a privacy-preserving "friend finder" application for Android. The reuse of previous inputs to allow stateful evaluation represents a new way of looking at SFE and further reduces computational barriers.

cs.CR↗

arXiv2015

Seeking Anonymity in an Internet Panopticon

Obtaining and maintaining anonymity on the Internet is challenging. The state of the art in deployed tools, such as Tor, uses onion routing (OR) to relay encrypted connections on a detour passing through randomly chosen relays scattered around the Internet. Unfortunately, OR is known to be vulnerable at least in principle to several classes of attacks for which no solution is known or believed to be forthcoming soon. Current approaches to anonymity also appear unable to offer accurate, principled measurement of the level or quality of anonymity a user might obtain. Toward this end, we offer a high-level view of the Dissent project, the first systematic effort to build a practical anonymity system based purely on foundations that offer measurable and formally provable anonymity properties. Dissent builds on two key pre-existing primitives - verifiable shuffles and dining cryptographers - but for the first time shows how to scale such techniques to offer measurable anonymity guarantees to thousands of participants. Further, Dissent represents the first anonymity system designed from the ground up to incorporate some systematic countermeasure for each of the major classes of known vulnerabilities in existing approaches, including global traffic analysis, active attacks, and intersection attacks. Finally, because no anonymity protocol alone can address risks such as software exploits or accidental self-identification, we introduce WiNon, an experimental operating system architecture to harden the uses of anonymity tools such as Tor and Dissent against such attacks.

cs.CR↗

arXiv2014

Representing Network Trust and Using It to Improve Anonymous Communication

Motivated by the effectiveness of correlation attacks against Tor, the censorship arms race, and observations of malicious relays in Tor, we propose that Tor users capture their trust in network elements using probability distributions over the sets of elements observed by network adversaries. We present a modular system that allows users to efficiently and conveniently create such distributions and use them to improve their security. The major components of this system are (i) an ontology of network-element types that represents the main threats to and vulnerabilities of anonymous communication over Tor, (ii) a formal language that allows users to naturally express trust beliefs about network elements, and (iii) a conversion procedure that takes the ontology, public information about the network, and user beliefs written in the trust language and produce a Bayesian Belief Network that represents the probability distribution in a way that is concise and easily sampleable. We also present preliminary experimental results that show the distribution produced by our system can improve security when employed by users; further improvement is seen when the system is employed by both users and services.

cs.CR↗

arXiv2012

On the Resilience of Routing Tables

Many modern network designs incorporate "failover" paths into routers' forwarding tables. We initiate the theoretical study of the conditions under which such resilient routing tables can guarantee delivery of packets.

cs.DC↗

arXiv2012

An Economic Analysis of User-Privacy Options in Ad-Supported Services

We analyze the value to e-commerce website operators of offering privacy options to users, e.g., of allowing users to opt out of ad targeting. In particular, we assume that site operators have some control over the cost that a privacy option imposes on users and ask when it is to their advantage to make such costs low. We consider both the case of a single site and the case of multiple sites that compete both for users who value privacy highly and for users who value it less. One of our main results in the case of a single site is that, under normally distributed utilities, if a privacy-sensitive user is worth at least $\sqrt{2} - 1$ times as much to advertisers as a privacy-insensitive user, the site operator should strive to make the cost of a privacy option as low as possible. In the case of multiple sites, we show how a Prisoner's-Dilemma situation can arise: In the equilibrium in which both sites are obliged to offer a privacy option at minimal cost, both sites obtain lower revenue than they would if they colluded and neither offered a privacy option.

cs.GT↗

arXiv2011

Probabilistic Analysis of Onion Routing in a Black-box Model

We perform a probabilistic analysis of onion routing. The analysis is presented in a black-box model of anonymous communication in the Universally Composable framework that abstracts the essential properties of onion routing in the presence of an active adversary that controls a portion of the network and knows all a priori distributions on user choices of destination. Our results quantify how much the adversary can gain in identifying users by exploiting knowledge of their probabilistic behavior. In particular, we show that, in the limit as the network gets large, a user u's anonymity is worst either when the other users always choose the destination u is least likely to visit or when the other users always choose the destination u chooses. This worst-case anonymity with an adversary that controls a fraction b of the routers is shown to be comparable to the best-case anonymity against an adversary that controls a fraction \surdb.

cs.CR↗

arXiv2010

Approximate Privacy: Foundations and Quantification

Increasing use of computers and networks in business, government, recreation, and almost all aspects of daily life has led to a proliferation of online sensitive data about individuals and organizations. Consequently, concern about the privacy of these data has become a top priority, particularly those data that are created and used in electronic commerce. There have been many formulations of privacy and, unfortunately, many negative results about the feasibility of maintaining privacy of sensitive data in realistic networked environments. We formulate communication-complexity-based definitions, both worst-case and average-case, of a problem's privacy-approximation ratio. We use our definitions to investigate the extent to which approximate privacy is achievable in two standard problems: the second-price Vickrey auction and the millionaires problem of Yao. For both the second-price Vickrey auction and the millionaires problem, we show that not only is perfect privacy impossible or infeasibly costly to achieve, but even close approximations of perfect privacy suffer from the same lower bounds. By contrast, we show that, if the values of the parties are drawn uniformly at random from {0,...,2^k-1}, then, for both problems, simple and natural communication protocols have privacy-approximation ratios that are linear in k (i.e., logarithmic in the size of the space of possible inputs). We conjecture that this improved privacy-approximation ratio is achievable for any probability distribution.

cs.CR↗

arXiv2010

Approximate Privacy: PARs for Set Problems

In previous work (arXiv:0910.5714), we introduced the Privacy Approximation Ratio (PAR) and used it to study the privacy of protocols for second-price Vickrey auctions and Yao's millionaires problem. Here, we study the PARs of multiple protocols for both the disjointness problem (in which two participants, each with a private subset of {1,...,k}, determine whether their sets are disjoint) and the intersection problem (in which the two participants, each with a private subset of {1,...,k}, determine the intersection of their private sets). We show that the privacy, as measured by the PAR, provided by any protocol for each of these problems is necessarily exponential (in k). We also consider the ratio between the subjective PARs with respect to each player in order to show that one protocol for each of these problems is significantly fairer than the others (in the sense that it has a similarly bad effect on the privacy of both players).

cs.CR↗