Trust in Crowds: probabilistic behaviour in anonymity protocols

The existing analysis of the Crowds anonymity protocol assumes that a participating member is either ‘honest’ or ‘corrupted’. This paper generalises this analysis so that each member is assumed to maliciously disclose the identity of other nodes with a probability determined by her vulnerability to corruption. Within this model, the trust in a principal is defined to be the probability that she behaves honestly. We investigate the effect of such a probabilistic behaviour on the anonymity of the principals participating in the protocol, and formulate the necessary conditions to achieve ‘probable innocence’. Using these conditions, we propose a generalised Crowds-Trust protocol which uses trust information to achieves ‘probable innocence’ for principals exhibiting probabilistic behaviour

Formal aspects of anonymity

technical reportWe present a formal definition of anonymity in the context of concurrent processes. The definition is given in category theoretic terms. Moreover, the concept of a split cofibration is shown to both simplify the analysis of anonymity as well as to increase the framework's expressiveness. Because of its categorical nature, our definition is largely independent of any specific model of concurrency. We instantiate the theory to two specific models, Hoare trace languages and probabilistic transition systems. By providing a semantics for CSP, the former model endows CSP with a definition of anonymity that applies to every CSP process simulation. The latter model, probabilistic transition systems, provides a definition of anonymity applicable to probabilistic processes. This seems to be the first general such definition

Anonymity Protocols as Noisy Channels

International audienceWe consider a framework in which anonymity protocols are interpreted as noisy channels in the information-theoretic sense, and we explore the idea of using the notion of capacity as a measure of the loss of anonymity. Such idea was already suggested by Moskowitz, Newman and Syverson, in their analysis of the covert channel that can be created as a result of non-perfect anonymity. We consider the case in which some leak of information is intended by design, and we introduce the notion of conditional capacity to rule out this factor, thus retrieving a natural correspondence with the notion of anonymity. Furthermore, we show how to compute the capacity and the conditional capacity when the anonymity protocol satisfies certain symmetries. We also investigate how the adversary can test the system to try to infer the user's identity, and we study how his probability of success depends on the characteristics of the channel. We then illustrate how various notions of anonymity can be expressed in this framework, and show the relation with some definitions of probabilistic anonymity in literature. Finally, we show how to compute the matrix of the channel (and hence the capacity and conditional capacity) using model checking

p-probabilistic k-anonymous microaggregation for the anonymization of surveys with uncertain participation

We develop a probabilistic variant of k-anonymous microaggregation which we term p-probabilistic resorting to a statistical model of respondent participation in order to aggregate quasi-identifiers in such a manner that k-anonymity is concordantly enforced with a parametric probabilistic guarantee. Succinctly owing the possibility that some respondents may not finally participate, sufficiently larger cells are created striving to satisfy k-anonymity with probability at least p. The microaggregation function is designed before the respondents submit their confidential data. More precisely, a specification of the function is sent to them which they may verify and apply to their quasi-identifying demographic variables prior to submitting the microaggregated data along with the confidential attributes to an authorized repository. We propose a number of metrics to assess the performance of our probabilistic approach in terms of anonymity and distortion which we proceed to investigate theoretically in depth and empirically with synthetic and standardized data. We stress that in addition to constituting a functional extension of traditional microaggregation, thereby broadening its applicability to the anonymization of statistical databases in a wide variety of contexts, the relaxation of trust assumptions is arguably expected to have a considerable impact on user acceptance and ultimately on data utility through mere availability.Peer ReviewedPostprint (author's final draft

Hang With Your Buddies to Resist Intersection Attacks

Some anonymity schemes might in principle protect users from pervasive network surveillance - but only if all messages are independent and unlinkable. Users in practice often need pseudonymity - sending messages intentionally linkable to each other but not to the sender - but pseudonymity in dynamic networks exposes users to intersection attacks. We present Buddies, the first systematic design for intersection attack resistance in practical anonymity systems. Buddies groups users dynamically into buddy sets, controlling message transmission to make buddies within a set behaviorally indistinguishable under traffic analysis. To manage the inevitable tradeoffs between anonymity guarantees and communication responsiveness, Buddies enables users to select independent attack mitigation policies for each pseudonym. Using trace-based simulations and a working prototype, we find that Buddies can guarantee non-trivial anonymity set sizes in realistic chat/microblogging scenarios, for both short-lived and long-lived pseudonyms.Comment: 15 pages, 8 figure

k-anonymous Microdata Release via Post Randomisation Method

The problem of the release of anonymized microdata is an important topic in the fields of statistical disclosure control (SDC) and privacy preserving data publishing (PPDP), and yet it remains sufficiently unsolved. In these research fields, k-anonymity has been widely studied as an anonymity notion for mainly deterministic anonymization algorithms, and some probabilistic relaxations have been developed. However, they are not sufficient due to their limitations, i.e., being weaker than the original k-anonymity or requiring strong parametric assumptions. First we propose Pk-anonymity, a new probabilistic k-anonymity, and prove that Pk-anonymity is a mathematical extension of k-anonymity rather than a relaxation. Furthermore, Pk-anonymity requires no parametric assumptions. This property has a significant meaning in the viewpoint that it enables us to compare privacy levels of probabilistic microdata release algorithms with deterministic ones. Second, we apply Pk-anonymity to the post randomization method (PRAM), which is an SDC algorithm based on randomization. PRAM is proven to satisfy Pk-anonymity in a controlled way, i.e, one can control PRAM's parameter so that Pk-anonymity is satisfied. On the other hand, PRAM is also known to satisfy ${\varepsilon}$-differential privacy, a recent popular and strong privacy notion. This fact means that our results significantly enhance PRAM since it implies the satisfaction of both important notions: k-anonymity and ${\varepsilon}$-differential privacy.Comment: 22 pages, 4 figure

Anonymity and Information Hiding in Multiagent Systems

We provide a framework for reasoning about information-hiding requirements in multiagent systems and for reasoning about anonymity in particular. Our framework employs the modal logic of knowledge within the context of the runs and systems framework, much in the spirit of our earlier work on secrecy [Halpern and O'Neill 2002]. We give several definitions of anonymity with respect to agents, actions, and observers in multiagent systems, and we relate our definitions of anonymity to other definitions of information hiding, such as secrecy. We also give probabilistic definitions of anonymity that are able to quantify an observer s uncertainty about the state of the system. Finally, we relate our definitions of anonymity to other formalizations of anonymity and information hiding, including definitions of anonymity in the process algebra CSP and definitions of information hiding using function views.Comment: Replacement. 36 pages. Full version of CSFW '03 paper, submitted to JCS. Made substantial changes to Section 6; added references throughou